Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other environment. In humanitarian environments the accessibility to electricity, internet, and qualified human resources is usually limited. Hence, advanced computing technologies in such an environment are hard to deploy and implement. Moreover, time and resources in those environments are also limited and devoted for life-saving activities, which makes computing technologies among the lowest priorities for those who operate there. In humanitarian crises, interests and preferences of decision-makers are driven by their original languages, cultures, education, religions, and political affiliations. Hence, decision-making in such environments is usually hard and slow because it solely depends on human capacity in absence of proper computing techniques. In this research, we are interested in overcoming the above challenges by involving machines in humanitarian response. This work proposes and evaluates a text classification and embedding technique to transform historical humanitarian records from human-oriented into a machine-oriented structure (in a vector space). This technique allows machines to extract humanitarian knowledge and use it to answer questions and classify documents. Having machines involved in those tasks helps decision-makers in speeding up humanitarian response, reducing its cost, saving lives, and easing human suffering.The online version contains supplementary material available at 10.1186/s41018-023-00135-4. Humanitarian response is a series of processes that aim at changing the state of crisis theatres from one of damage and destruction to one of relief and recovery. It is a complex operation of bringing life back to normality and easing the suffering of affected people. Treating patients, rescuing people, and saving lives are examples of what humanitarian response is about. Every process in humanitarian response produces large volumes of data that are captured, processed, used, and reused by humanitarian actors.Humanitarian actors keep records on who responded to humanitarian crises, what response they provided, and where, when, why, and how they responded. They often save such information in a semi-standard data structure called the 6Ws, in which they preserve all aspects of their response in the form of questions and answers. The 6Ws data collection instrument and data exchange standard is used by humanitarian actors across the globe. It has been derived from the best practices used in education, journalism and law enforcement fields for survey and investigation . Hence, often the quality is sacrificed in favour of speed.Domain experts are deployed to work in harsh and unsafe environments, where civil wars, diseases, hunger, and other harmful realities are present. Deploying human agents to do such work in every humanitarian environment is a dangerous mission as well.The existence and evolution of computing technologies in the past few decades has done very little to change the way questions are answered in humanitarian contexts. Human agents are still employed by organisations to answer questions in humanitarian crises in the absence of appropriate computing technologies. According to Fernandez-Luque and Imran , Imran eReducing the cost and time required to answer questions in humanitarian crisis could help humanitarian communities save more resources and time, which could be channelled towards the original mandate of these organisations, i.e. saving lives and reducing suffering.Uses a humanitarian text corpus to train a model on text classification based on a set of labels used to annotate observations. This text corpus has to be normalised to ensure that each document refers to a controlled set of labels. Using label annotations in text classification allows our model not only to retrieve known entities but also to predict and retrieve unknown entities based on the most plausible predictions.Produces a neural text classification model through which questions can be answered, entities can be classified, analogies can be solved, and words can be embedded for humanitarian domain-specific contexts. This model should be able to transform unstructured plain text corpora into useful knowledge in a structured data format (with no human intervention).Produces a humanitarian context-based word-embedding, in which related words can be placed closed to each other based on their humanitarian relations and context. For example, in this word embedding, the word “Japan” must be placed close to the word “Nuclear”, “JICA”, “Tsunami” and “Earthquake”, while in a generic word embedding “Japan” will be placed close to “Japanese”, “Korea”, “Tokyo”, and “China”. Having similar words placed in close proximity to each other allows knowledge retrieval at a later stage. In knowledge retrieval we use geometric distance, such as CosineGiven that we still need to have classes—and their affiliated words—placed in the right classes, we need to have a class embedding technique. Each entity in our model must belong to one of the classes. Our model uses a class embedding to connect classes and labels, associate them with corresponding texts, and optimise the distances between similar entities, classes, and properties to ensure that knowledge representation, in the model, matches the real humanitarian crisis environment. For example, if World Vision and Red Cross are close to each other in the real world , they must be placed close to each other in our model’s vector space.We propose to embed humanitarian data in a continuous vector space, in which we place semantically similar concepts in close proximity to each other and then use geometric interpretation to infer knowledge from this vector space in order to augment the role of domain experts and enhance decision-making in humanitarian crises. To transform such knowledge into a vector space we may:Using such a model we show that advanced computing techniques (such as machine learning) can be utilised to extract knowledge and insights from abandoned historical humanitarian records. This knowledge can be used by human agents in current and future crises to facilitate better, faster, and cheaper humanitarian response.In the next section, we review and discuss related work, in which we explore three areas knowledge acquisition, knowledge representation, and knowledge extraction. In “Dataset description” section, we explain how we obtained, cleaned, processed, and prepared a humanitarian dataset that covers the period between 1994 and 2016 to be used in model training. In “The model” section, we present the mathematical model that we developed and used in this research and also illustrate the neural architecture of the model. In “Results visualisation” section, we infer and visualise the hidden semantic relations between humanitarian classes and documents. In “Evaluation” section, we evaluate the model: (1) qualitatively, though semantic measurement between humanitarian entities and document classification; and 2) quantitatively, through evaluation of the model accuracy across eight classes: agency, actor, SDG, sector, place, month, year, and reason using several techniques, including transformers. Finally, in “Conclusion” section, we conclude this paper and briefly discuss our results and achievements.Using advanced computing technologies, such as Machine Learning (ML) and Natural Language Processing (NLP), in the humanitarian sector has gained popularity in recent years. The reason for this popularity can be attributed to the desire of humanitarian organisations to speed up, reduce the cost, and improve the quality of their operations. Using advanced computing technologies, in such a process, helps in achieving these goals, as lessons learned from other sectors shows , how this data can be transformed into knowledge (“Knowledge representation” section), and how this knowledge can be used to improve humanitarian response (“Knowledge extraction” section).In humanitarian crises, acquiring reliable data is an essential task that humanitarian actors have to perform to enhance their decision-making stance. Sources of this data are varied, they might be collected from any viable source, including free text from social media, images from drones and satellites, and tabular data from APIs and websites. Most of the data sources, in humanitarian crises, are a mixture of different types, hence, different methodologies should be used with those data sources.It is not enough to work on free text, using natural language processing to extract humanitarian knowledge while ignoring tabular data or aerial images, which hold a lot of humanitarian knowledge. Combining different data types together improves their quality and reliability. The quality of decisions made in humanitarian crises, relies—to a great extent—on the quality of the data used in making those decisions. In this section, we explore how data is acquired from different sources and how they are processed to produce reliable information that can be used by decision-makers to improve crises theatres.Social media is an open source of humanitarian information. Social media platforms are often used by journalists, practitioners, and researchers to obtain timely information about ongoing crises. Imran et al.  conducteBesides social media, aerial data is another source of information in humanitarian crises. The deformation of terrains and damages of infrastructures in crisis theatres is a phenomenon that can be monitored and observed from open skies. Using aerial images to examine the impact of crises on earth is an approach that has been used for years to collect information about humanitarian crises. Ofli et al.  proposedQuinn et al.  used macIn some cases, data sources of different type are fused together to establish a multimodal data source. Ochoa and Comes . We also have a label for each month in the year in which interventions took place. For each intervention, we have at least one reason out of 21 generic reasons for intervention, e.g. flood, earthquake, drought. At a later stage, we used semantic sentence encoding to enrich our dataset by adding 17 Sustainable Development Goals (SDGs).Inspired by the work done in semantic document comparison, in particular the work of Carter et al. , we deveWe also used text augmentation to amplify our dataset. Text augmentation is an NLP technique to amplify small text corpora, which is a common problem in domain-specific studies  field in the dataset. The dictionary consisted of 145,833 unique words and phrases. We used the 10,000 most-used words out of these unique words and phrases.Our mathematical model in this research is highly inspired by the work of Goldberg and Levy  which isThe work of Mikolov et al.  used twowO is the context words and wI is the target word, while wO, wI) is too large. Hence, a new method was proposed , and minimise the probability of the negative samples, in (D').For clarity, we present the formalisation of this approach by Goldberg and Levy  who starAccording to Goldberg and Levy , Skip-Grd with a set of labels L, i.e. one input document d to many output labels L, while in function (2) the optimisation is taking place between target word (w) and context word (c), i.e. one input word (w) to one output word (c). The loss function, in Eq.  ∈ T, and penalises the wrong tuples  ∈ T′.The difference between our work, in function 3), and the work of Goldberg and Levy , in Eq. , and theFigure We added a new binary label to our dataset, we call this label “State Label”,Since observations in the dataset are correct, i.e. positive, their state label must be equal to one,In every training epoch the model is learned on positive samples, but also generates a virtual batch of negative samples, i.e. wrong labels,For negative samples, i.e. wrong labels, we made the value of state label equal to zero, andActivate positive samples  and,Penalise negative samples .We trained the model, and used a sigmoid function, to:In our implementation of negative sampling:Using this technique, we managed to push away the wrong labels in the embedding and keep correct labels close to their corresponding documents.The first layer is the Input Layer through which the vocabulary is passed into the network in batches of documents, each consisting of 100 words, in each training iteration. The total number of unique words in our text corpus is 145,833. However, for efficiency reasons, we used only the 10,000 most-used words (excluding stop words).each word in the document is represented by a vector of 300 dimensions) or dense vector (where the entire document is represented by a vector of 300 dimensions) depending on the technique used to produce the document embeddingThe second layer is the Embedding Layer, which maps each of the 10,000 inputs  to a vector of 300 dimensions. The output of the Embedding Layer can be a sparse vector  was designed to associate text inputs (documents) with tabular outputs (labels). The model was trained, validated, and evaluated using a corpus of 52,808 historical humanitarian observations. The model consists of one input layer, one embedding layer, and eight output layers:An additional output (state label) was added to the model for Negative Sampling. This is a probability layer which has a binary output classification value of 1 or 0. It has been used to maximise the weights of positive samples (correct observations) and penalise the negative samples (wrong observations) Fig. .Fig. 3SEEach of the 10,000 words in our vocabulary list has been encoded into a unique index, and handed over to the Embedding Layer to convert them to vectors, which are either randomly initialised or obtained from pre-trained embeddings (more details in “Quantitative results” section). Those vectors are then passed to the output layers.Each output layer in SEmHuS responds to one of the 6W questions: actor and agency layers respond to the question of “Who”, goal, i.e. SDG, and sector layers respond to “What”, place layer respond to “Where”, year and month layers respond to “When”, and reason layer respond to “Why”. The last question of “How”, i.e. subsector, has no specific label in the dataset, as it comes in the form of narrative text, which we used as an input layer in the model.W in the document embedding matrix, which come from the Input Layer (documents) towards the Embedding Layer H. The second embedding is extracted from the transposed weights H towards the Output Layer (labels).SEmHuS produced two embeddings: a document embedding and a class embedding. The first embedding is extracted from the weights W holds the semantics of the documents used to train the model, where each word in this embedding has a vector of 300 dimensions. Meanwhile the transpose of the second embedding Vc holds the semantics of the labels, where each label in the model acquires a vector of 300 dimensions. Figure The embedding The matrix Vc implicitly records the semantics of the labels. It holds the relative distances between the embedding vectors of labels in eight classes: agency, actor, SDG, sector, place, year, month, and reason, providing semantic relations between those classes. Using this embedding to explore such relations helps in finding most similar entities in our model outputs. When two organisations, for example, operate in the same country, in the same year and month, doing the same or similar interventions, for the same reason, for the same SDG, that indicates they have a similar humanitarian history.Embedding This component is able to “infer knowledge” from humanitarian history.❖ Class embedding: finds semantic similarity between entities used in records’ classes using a domain-specific, class embedding in which humanitarian labels are placed close to each other based on humanitarian history through which they passed together in the past. Text embedding can measure semantic similarities, solve analogies, and discover unknown concepts.❖ Text embedding: finds semantic similarity between words used in records’ descriptions using a domain-specific, word embedding in which words are placed close to each other based on humanitarian relations between them. SEmHuS can “answer questions”.❖ Question answering: predicts results and extract knowledge from unstructured data sources. SEmHuS accepts questions from end users, as free text, and reasons about them to provide most plausible answers. SEmHuS can “classify documents” and produce tabular results from plain texts , solve analogy (“Document embedding” section), and discover unknown concepts (“Class embedding” section).This experiment was driven by our interest in discovering what our results look like in a two-dimensional space: what are the hidden relations between different documents in humanitarian contexts, and what can be revealed in a visual representation of results.We used a t-SNE to transform output vectors into visualisations by reducing the dimensionality of the vectors (from 300 to 2 dimensions). t-SNE is a “technique for visualisation of similarity data that is capable of retaining the local structure of the data while also revealing some important global structure ”  only, using the t-SNE technique (described in page 89), and then,Plotted each observation on a two-dimensional canvas (using the Seaborn library in Python) as shown in Figs. We transformed the output vectors into visualisations in three steps. In these steps, weDots, in the five visualisations, represent classified documents, where each document has eight classes: actor, sector, etc. Each visualisation represents one class and colours represent labels of the respective class. Colours, legends, and labels change from one visualisation to other while positions of the documents, i.e. dots, remain fixed.The motivation to perform this task lies in four hypotheses we came to during this research: crisis seasonality, humanitarian mandates, humanitarian and development overlap, and cause and effect. Document visualisation provides a visual tool to evaluate these hypotheses, which will be explained and visualised in the next sections.We hypothesised that similar crises take place in similar seasons. For instance, we presumed that tropical cyclones have seasons to take place in, while epidemics (such as dengue fever and flu) have different seasons. Hence, the visualisation of the reasons for crises should be close to the season of those crises.While Fig. To further examine correlations, we transformed the above visualisations into a matrix, Additional file Table A. On the other hand, the visualisation of the reasons in Fig.  and the Figure By comparing these two visualisations, using Table The intersection between development and humanitarian domains is an active research area  and cause (reason). The matrix is in Additional file In this table, we can see that the nearest reason to health sector is epidemic, to logistics is storm surge, to agriculture is insect infestation and drought, and to shelter and non-food items is cold wave and snow avalanche. These are just few examples among many in Table A. The above visualisations show glimpses of the visual that SEmHuS enables. Many other combinations can be performed to show the overlap between different classes analysis in our model. By mixing different visualisations together, SEmHuS can produce up to 56 different comparisons of similarity to the above ones. Using those visualisations to present overlaps among actors, places, sectors, timeframes, and reasons provides a useful tool for the humanitarian community to use in supporting their day-to-day work.In this section, we used two evaluation methodologies to evaluate our proposed technique: quantitative and qualitative evaluations. While the former is a numerical comparison between results, the latter is more of a narrative and descriptive form.In quantitative evaluation, we assess the quality of our results using several text embedding techniques. For qualitative evaluation we conducted two information retrieval tasks to assess the ability of our model to support and augment the role of domain experts in humanitarian crises.As explained earlier, we transformed the documents in our dataset into vectors, which we used to train and optimise the model. There are several techniques to obtain those vectors; we used two prominent techniques that are used in similar tasks: (1) random initialisation, and (2) pre-trained vectors.The first technique is a word average vector embedding technique, in which we create a vector for each word in our dataset, then randomly initialise those vectors, converge vectors through training, and consolidate them (either through concatenation or averaging) to create a unified vector for the document containing those words. Unlike the other techniques we evaluated, this technique does not borrow weights from pre-trained embeddings, but rather creates them from scratch. In the first model (AvgVec), the document embedding is obtained by averaging word vectors and using the obtained vectors to represent documents. Each document vector obtained from this technique is optimised to predict corresponding labels for each document. This embedding is generated using the Keras Embedding Layer. This technique is also used to obtain document vectors from other models, such as Bidirectional Encoder Representations from Transformers (BERT)   in which document vectors are acquired by training a model to predict target words in a document using context words plus paragraph ID then averages (or concatenate).The second technique is paragraph vector with distributed bag of words (PVD-BOW), in which the document vector is obtained by training the model to predict all the words in a paragraph using paragraph ID.The first document embedding technique, Doc2Vec, is a shallow learning technique derived from the work of Le and Mikolov . In thisThe second document embedding technique, AvgVec, is also a shallow learning technique is, in which we use word vector averaging, where word vectors are randomly initialised and trained to obtain word vectors. By the end of the training process, we get a by-product word embedding.Meanwhile, the third and fourth embedding techniques (NNLM and USE) both use a vector Deep Averaging Network (DAN). The difference between Doc2Vec, on the one hand, and NNLM and USE, on the other, is that Doc2Vec is a shallow learning technique  tasks  ≈ vec(King) − vec(Man) + vec(Woman)  ≈ vec − vec(Rome) + vec(Paris).In this task, we used the vec(Man) is to vec(King) as vec(Woman) is to vec(Queen).According to Allen and Hospedales  these anIn this task we used a set of analogies to retrieve documents by subtracting the vectors of unwanted terms, and adding vectors of new terms, to the document vector. For example, if we have a document, i.e. Doc 1, and this document is about “Ebola in West Africa”. We would like to find out what document will be the most similar to this document if we deducted the vector of the word “Ebola” and added the vector of the word “Cholera” to the document. We expect the result, of such substitution, to be a vector of a document Doc 2, about “Cholera in West Africa”, or a similar result, as shown in Eq. .4\documevec(Doc 1) – vec(Unwanted Term) + vec(Wanted Term) on the one hand and vec(Doc2) on the other hand. In Table To test this assumption, we randomly selected 10 documents from our dataset. We checked each one of them, manually, to decide what term to deduct and what term to add. We used our model to vectorise those documents and then used the NumPy library (in Python) to conduct the arithmetic operations, i.e. vector addition and deduction. We used the L2 Norm, i.e. Euclidean Distance, to measure the distances between Passing this set of documents, in Table vec(Source Document) – vec(Unwanted Term) + vec(Wanted Term), has the shortest Euclidean distance to the target document, i.e. vec(Source Document).Each target document, in Table We notice, in Table We manually inspected the target documents to ensure that the model has retrieved reasonable results. In this inspection process we found a strong semantic relation between source documents and their corresponding target documents.For instance, in the first result, we used the model to deduct the vector of the word “flood” and added the vector of the word “earthquake”, to the vector of first document, in Table Another example is in the fourth result, in which we replaced the vector of “Nepal” with the vector of “Syria”. While the source document was discussing UNHCR interventions in Nepal, the target document was about “Refugees left out in cold in Middle East”. The model found that when the vector of the word “Nepal” is deducted from the vector of a document discussing UNHCR work in Nepal, and then added the vector of “Syria” in its place, the resulted vector of the target document was so close to the original topic, i.e. refugees, which has been discussed in the source document.The last example is the seventh result, in which we deducted the vector of the word “refugees” and added the vector of the word “children” to the source document. The inspection of this result shows that the source document was discussing refugees in the Dominican Republic, while the target document was about “Improving Shelters for Child Victims of Trafficking in the Dominican Republic”. In this example, we manually inspected the resulting, i.e. target, document to ensure that unwanted term is truly eliminated and replaced by the wanted term. While the source documentVc embedding from Fig. In this task, we used the Vc (the transpose of the embedding layer),Generated a set of random entities, then extracted the vector of each one of those entities from matrix Measured the Cosine distance between the vectors of those entities to find the nearest neighbouring entities to each entity, andOrdered the retrieved entities from the nearest, i.e. shortest distance to the remotest, i.e. longest distance.Our model optimises the vectors of entities that have similar humanitarian co-occurrence, to lie in close proximity. To test and verify our model, weMeasured the mean distance and standard deviation for all pairs of entities we have in our dataset,z-score, for every query, the z-score shows how many standard deviations are between the mean and the observed value (the distance),Used the mean and the standard deviation to compute a α), i.e. a z-score value, where values (distances) lower than α are accepted and higher values are rejected, andSet a threshold  geographical vicinity of the countries, (2) functional relations between organisations, (3) chronological order for timeframes, and (4) consequences of the disasters.As shown in Table The model has never been informed that the Samoa is geographically close to Fiji and Tonga, that a tsunami is caused by an earthquake, February is close to March, April, and January, New Zealand Red Cross is similar to Australian Red Cross, or Zero Hunger (SDG 2) is most related to Decent Work (SDG 8). None of these meaningful relations were passed to—or imposed on—the model at any stage. They were discovered and captured by the model during the training process.However, some of the similarities, in Table Moreover, knowing that one of the most devastating tsunamis in recent history occurred in Japan in 2011, in the city of Fukushima, causing meltdown in three reactors of the city’s nuclear power plant, explains this unexpected similarity between tsunami and technological disaster.our model has developed its own sense of semantic connection that makes it able to relate similar humanitarian entities to each other, without human intervention, and produce new knowledge out of the raw humanitarian records.This useful knowledge has been acquired by the model during the training process. These results show that In this paper, we developed a machine learning model through which the divide between historical humanitarian crises and existing ones is bridged. We built a model that translates uncontrolled vocabulary and unstructured humanitarian text into classified structures. We transformed a dataset of documents and classes into a vector space, in which semantic similarities between humanitarian entities were established, measured, and used to improve our understanding of the interactions of humanitarian entities in humanitarian environments. A unique approach has been established to extract meaningful results from historical humanitarian records. Out of six NLP tasks, listed in “Model potential” section, we tested SEmHuS against three of them  and achieved meaningful results. Moreover, the embeddings visualisation, in “Results visualisation” section, revealed some hidden relations between humanitarian concepts, such as crisis seasonality, humanitarian mandates, humanitarian and development overlap, and cause and effect of humanitarian crises. We managed to bring this knowledge to the light and use it to understand the underlaying semantic relations between humanitarian concepts. Those relations used to be buried—in the past—under piles of historical humanitarian records. This revelation helps in making future humanitarian responses faster, cheaper, safer, and more efficient.Additional file 1: Distance correlation matrix. Table A. 1. Crises seasonality distance correlation. Table A. 2. Humanitarian mandates distance correlation. Table A. 3. Humanitarian and development distance correlation. Table A. 4. Cause and effect distance correlation.


Across the globe, comprehensive COVID-19 vaccination programs have been rolled out. Naturally, it remains paramount for efficiency to ensure uptake. Hypothetical vaccine acceptability in South Africa was high prior to the availability of inoculation in August 2020—three-quarters stated intent to immunize nationally. However, 24 months on, less than one-third have finished their vaccination on a national average, and in the sprawling South Western Townships (Soweto), this figure remains troublingly low with as many as four in every five still hesitant. Medical anthropologists have recently portrayed how COVID-19’s jumbled mediatization produces a ‘field of suspicion’ casting serious doubt on authorities and vaccines through misinformation and counterfactual claims, which fuels ‘othering’ and fosters hesitancy. It follows that intent to immunize cannot be used to predict uptake. Here, we take this conceptual framework one step further and illustrate how South African context-specific factors imbricate to amplify uncertainty and fear due the productive nature of communicability, which transforms othering into racialization and exacerbates existing societal polarizations. We also encounter Africanized forms of conspiracy theories and find their narrational roots in colonization and racism. Finally, we discuss semblances with HIV and how the COVID-19 pandemic’s biomedicalization may inadvertently have led to vaccine resistance due to medical pluralism and cultural/spiritual practices endemic to the townships. In December 2019, a novel virus emerged causing unprecedented cases of severe acute respiratory syndrome (SARS) in Wuhan, Hubei Province, China. One month on, in January 2020, the World Health Organization designated this illness as 2019-nCoV—retitled SARS-CoV-2 in February 2020—and then onwards referred to it widely as COVID-19, coronavirus disease 2019 .Globally, governments implemented an array of socially restrictive measures to curb the spread of the virus, including stringent use of masks; regular handwashing and sanitizing; physical distancing, popularly referred to as ‘social distancing’; and, in some countries, even gender-based limitations on pedestrians sought to reduce movement ,5. SimilInitially, surveys noted levels of hypothetical vaccine acceptability as high as 75 percent—meaning that three-quarters expressed acceptance towards inoculation were vaccines to become available ,7. PreseReasonably, from the onset, the World Health Organization “included vaccine hesitancy in its top ten global health threats” . It has In a recent study on aspects of vaccine acceptability in South Africa, we found significantly elevated levels of hesitancy and denialism in the South Western Townships (Soweto) and presented generalizable evidence that stated intent to immunize does not predict uptake (see ). In thaAn exploratory qualitative inductive study was conducted in Soweto and Thembel-ihle, Gauteng Province, South Africa in August 2020, specifically in the township clusters of Braamfischer, Emndeni, Mapetla, Meadowlands’ zones four and five, Mofolo North, Phiri, Senaone, and Thulani townships. Soweto consists of a cluster of 29 townships and has a population of around 1.7 million people. It is populated mainly by low-income Black-African communities of predominantly Zulu, Xhosa, Pedi, Venda, Tswana, and Sotho ethnicities . EthnogrBoth the efficacy of preventive measures and the successfulness of vaccination rollouts depend heavily on whether the public receives messages, finds them credible, accepts them, and adheres to the advice of government and health authorities. Appropriate awareness about inoculation during a pandemic therefore shapes social and care-seeking behaviors. Hence, one key variable in combatting the virus is how it is mediatized by those with access to and the means of producing the dominant, overarching narrative about it. Meanwhile, dis- and misinformation is detrimental to both preventive measures and vaccine acceptancy as counterfactual claims and conspiracy theories sow doubt and uncertainty, which, in turn, generates a ‘field of suspicion’ towards immunization and authorities with levels of misperceptions resembling those seen in the early days of the HIV pandemic. With structural violence, as we shall see in Soweto, this disproportionately affects those with poor access to information and healthcare services already marginalized in a socioeconomic mire. These imbricated factors compound with certain site-specific cultural and historical contexts of the townships, which, somewhat surreptitiously, amass to amplify a field of suspicion towards COVID-19 and vaccination to the point where two-thirds the population (⅔) have been swayed into hesitancy and denialism due to confoundment, incertitude, and doubt, leading to adoptions of ‘alternative forms of knowledge’ . This then triggers a social mechanism medical anthropologists refer to as ‘othering’, which, as we found and illustrate here, evolved into more sinister shapes in Soweto, namely those of racialization, Africanized conspiracy theories, and societal polarizations due to the reproducible nature of communicability and a strict biomedicalization of COVID-19 where traditional medicine is widely used. These are highly detrimental to vaccine confidence and fuel hesitancy considerably and, consequently, must be addressed to boost uptake in present immunization programs. As such, appropriate mediazation, alongside a debunking of counterfactual claims, is pivotal in driving forward inoculation.Statements recorded in Soweto regarding clinical knowledge about COVID-19’s modes of transmission, symptoms, vulnerable groups, and actions in event of illness generally correlated with what might be characterized as relatively good awareness, even with respect to new variants. This echoes findings of other studies undertaken throughout South Africa ,14 and eRegarding modes of transmission, most community members knew that people may contract COVID-19 by way of touching, proximity, airborne saliva droplets, handshakes, or not wearing a mask.If someone with COVID touches a paper, and I come and touch the same paper without disinfecting it, I expose myself to the virus, and I can be infected. (Focus group two)You can get infected if you’re in a crowded place without wearing a mask and not practicing social distancing. (Focus group eight)[Through] shaking hands and by coughing—when you cough without covering your mouth. (Key informant four).We’ve been told to always wear a mask and wash our hands, because it’s passed on through coughing; those droplets can be passed on to the next person as well as their sneeze. (Focus group nine)Sowetans described COVID-19 symptoms as cold spells, fever, weakness, fatigue, and a loss of taste or sense of smell, though some symptoms were not mentioned at all, including myalgia, malaise, nasal congestion, expectoration, rhinorrhea, headaches, or nausea The patient will be coughing; some will experience shortness of breath. (Key informant four)Of course, some groups are more vulnerable than others, including “racial/ethnic minorities, children, the elderly, immigrants/refugees, those who are socioeconomically disadvantaged, disabled, underinsured, from rural communities, incarcerated, facing domestic violence, LGBTQ+, and [those] with certain medical conditions.” ,20,21. AMy mum, because she’s diabetic, and they said that people who’re diabetic or sick have weak immune systems, which puts them at a higher risk of catching the virus. A person with a chronic disease is one with diabetes, high blood [pressure], HIV, and those who have cancer. It’s anything that’s chronic in your blood. (Focus group three)If it [COVID-19] finds you in good health and your immune system is strong, that’s when it leaves you alone, but if you’re unhealthy, that’s when it can even kill you within three days. That’s the challenge we have with this new virus. (Focus group nine)One notion that added considerably to anxiety, however, was that an individual may be infected with COVID-19 and remain asymptomatic while contagious.The government said people can contract the virus and show no signs but can pass it on to the next person. (Focus group six)At times, you can be positive for COVID, but not see those symptoms. (Focus group eight)Asymptomatic patients are indeed hard to trace, unlikely to self-isolate, and more likely to retain normal social patterns, making this preoccupation comprehensible . With reI know that when you’ve contracted it, then you have to quarantine—when it’s full at the hospital, take care of yourself, and practice social distancing until you recover. (Focus group five)It soon became clear that community members were eager to keep abreast of developments and information regarding the pandemic. Some accentuated how they kept themselves updated through largely unregulated social media.[People] know about the virus, they are aware of it through the media, social media. (Focus group nine)Regarding mediatization, as Briggs points out, people do not exist in societies with media, but rather in “mediated societies where images of self and society are shaped by the media.”  The publFor this, Fairhead et al. employs a term, ‘fields of suspicion’, in which the decision to inoculate is taken within a field of “uncertainty and speculation in which wider confidence or worries are relevant” . For MarIn Soweto, early on, some mentioned that they had been following COVID-19 on television and seen events unfold in other countries:My family was very frightened. At first, we didn’t know what this COVID-19 is, but from watching the news—Italy, China—people are dying, and then we’re like: ‘no, people are dying; we are not going to go out [of the house]’. (Focus group eight)Some community members were aware of the presence of COVID-19, but due to deep uncertainties about it, this awareness fostered only fear.I’m still very scared of Coronavirus, even today, because I’ve not yet understood what it is or when it’ll be cured. (Focus group nine)There’re still people who don’t understand anything about COVID, yes, they know it kills, but they don’t where it’s from or how it arrived. (Focus group six)In contemplating these anxious statements, firstly, we should be mindful of the townships’ levels of access to formal media and, secondly, of barriers to comprehending health-related messages caused by the plethora of languages spoken throughout Soweto.No, in my community, there aren’t a lot of people who have access to televisions. (Focus group six)I’m 99 percent sure that in our community, our people, there is a communication barrier—people from Malawi, people from Zimbabwe, people from Maputo [Mozambique], others from other countries, they come. How brave they are! They come to South Africa, no visa, no nothing, just coming. Now when you try to initiate communication, they just smile. So, how are you going to give that person information? So, it becomes a problem. (Key informant four)‘Ignorance’, as one youth characterized it, or ‘lacking interest’, were also touched upon as hurdles to knowledge acquisition. One man’s experience was that:We had pamphlets that were being distributed, but people were taking those papers, and would not even read [them]. You’d find them on the floor, and you know, sometimes, you’d ask a person: ‘what does this thing say’, and they would respond saying: ‘hay, I dunno, I just saw it here.’ So, ignorance and misinformation are the most important reasons why people fear this disease. (Focus group seven)So, once an infectious disease appears, health surveillance and control systems are activated, which includes the propagation of relevant and accurate information to the public to prevent further infection and ensure that appropriate preventative behaviors are adopted and practiced ,28,29. SAs touched upon while discussing mediatization, the wavering narratives we recorded in Soweto are rooted in the production and reception of knowledge about disease, meaning in its mediatization or ‘construction’ of public discourse. This communicative process is ideologically constructed in such a way “as to make some people seem like producers of knowledge, others like translators and disseminators, others like receivers, and some simply out of the game” . For thiCounterfactual claims have certain shared aspects—ill intentions are implied, ‘something is off’, and there is a (hidden) agenda or motive as to why this or that is happening. Moreover, conspiracy theories are solipsistic and immune to evidence and “inherently self-sealing—evidence that counters a theory is reinterpreted as originating from the conspiracy theory” . MeanwhiIn these alternative realities, COVID-19 fatalities are exaggerated on purpose; it was a scheme concocted by ‘Big Pharma’, or rather it was thought up by shadowy entities of the ‘deep state’, while some believe it does not exist at all. In other iterations, the United States’ army covertly brought it into China, whereas some have it that it escaped from a laboratory there, or that Bill Gates put microchips in it to ‘control’ people, or that the 5G network caused it. Others again claim that COVID-19 is in fact a biological weapon run amok or that the virus sprung from genetically modified crops. In Soweto, we discovered several local or ‘Africanized’ counterfactual claims.When such misinformation abounds, alongside uncertainty and doubt in a growing field of suspicion, does it not become more understandable why so many might hesitate and postpone immunization, perhaps indefinitely?When COVID arrived, I saw a lot of things—the way people were ignoring COVID. Because when COVID arrived, there was lots of false information circulating through the social networks. (Focus group one)In the early days of the COVID-19 outbreak in South Africa, despite government and health authorities airing newsflash after newsflash monitoring its rapid spread across the country, our informants openly questioned whether the virus was real.In my location , there’re many people who don’t believe that the virus is real. They only believed it when many people started dying and by then the virus had already spread everywhere. In Mofolo, they don’t believe COVID is real. They’ll tell you that it doesn’t exist. (Focus group two)With beaming round spectacles, one thoughtful man speculated that a relatively low number of fatalities in the communities was contributing to people questioning the disease’s existence.Only few people died in our community, and this made people think that the virus isn’t real. (Focus group three)I also used to say that this thing doesn’t exist, more specifically when it started, and they showed other countries. I just assumed that it was something far off because no one knew anyone who was infected at the time. (Focus group six)That the COVID-19 outbreak began in Wuhan, China in December 2019 at the Hunan seafood market soon became a well-established fact among informants. However, the presence of unsubstantiated rumors and doubts as to exactly how the virus came about in China led to scapegoating and stigmatization of those of Asian descent as an outcome of fear, blame, and finger-pointing visible at the time as increased hate crimes against Asians . One comAnimals?! That’s what many say. I even saw it in a movie that the virus came from animals. (Focus group three)they eat dogs? (Focus group two)Is it true that the Chinese got the virus because They, the Chinese eat everything, snakes. (Focus group three)they ate pigs, and some ate frogs. (Focus group five)They said it originates from China, First, notice above the underscored ‘they’ (as an opposition to ‘us’). Here, we see a social process that medical anthropologists have labeled as ‘othering’ . As a beI think the reason why Corona is growing is because of denial—believing that there’s no such thing as Corona and that it won’t kill you. That’s what they say in the townships: ‘it’s the Chinese disease’. (Focus group three)Yes, the rumors are many. At first, I was afraid. At China Mall (in Johannesburg) even now, if you’re climbing into a taxi, they say you’re going to ‘Corona City’. I was afraid. I only go, because I have to, but I’m afraid of that city. (Focus group three)It is the Chinese fighting over the 5G. We heard those stories—Trump and China. (Focus group three)In a slur, Donald Trump, then democratically elected president of the United States of America, referred to COVID-19 at the time as the ‘Kung-Flu’—blatant racialization, which has been defined as “the extension of racial meaning to a previously unclassified relationship, social practice, or group” . ProblemOther people were sharing cigarettes and traditional beer, saying: ‘hay, that thing doesn’t exist’. ‘I am Tsonga and I believe in my culture; it will not catch me’. (Focus group five)They would tell you that: ‘we’re not the same as other countries; we’re eating the foods that we want, we eat masonja [mopane worms], we eat gushe [samp], meaning ‘I’m strong, because I’m from Limpopo’. (Focus group five)Gesturing with her hands, a spirited woman underscored the belief that the genetic make-up of Black Africans would shield them from infection.We have the mentality that, we as Black people, our immune system is strong. They’d mention that we as Black people have strong genes and our immune system is strong. (Focus group one)This instance of racialization can be found throughout Africa, involving notions that “African blood is stronger” or “our blood is of higher quality than that of White people” . A girliZulu people say that COVID only infects Whites, because they are weak, but not them, because they are extraordinarily strong. (Focus group one)It only killed White people and we as the other races are very safe. (Focus group five)Connecting racialization and medicalization through communicability “reveals how their power is derived partly from ideologies of communication with which notions of race and health are imbricated” . The WhiAs illustrated, most Sowetans believed COVID-19 to somehow ‘come from China’. A grizzled woman, visibly unsettled by the topic, concurred with this sentiment, but added yet another preoccupation regarding the pandemic’s origins.I believed that it was something manufactured by them [the Chinese]. Us? We know that it’s man-made! (Focus group five)This exact view was repeated endlessly.Some will say that’s something that has been concocted by White people in the air; they want to kill us! The mere fact that it’s taking them this long to find a—I’m not sure—a cure. Yeah, that’s the problem. I think it’s man-made. (Focus group eight)they want to kill us’ (Blacks)—othering transitioned into racialization wrapped up in counterfactual claims of the virus being ‘man-made’. For a fully-fledged African conspiracy, all we need now is to pile on nefarious intent, preferably with underlying malicious motives. Others in that same focus group engaged and provided those motives, echoing that:Here, again, ‘the other’ (Whites) has put ‘something’ in the air and ‘The population is too much [too numerous]; that’s why they’re trying to reduce it. (Focus group eight)They’re trying to start from scratch. It is a … chemical [biological] bomb! (Focus group eight)Eventually, an African extinction agenda was plainly alluded to, specifically in comments concerning a certain conspiracy to annihilate (Black) Africans and how African leaders were responding to it.There’s a vaccine that’s being distributed throughout Africa and you Africans shouldn’t take that vaccine; they’re trying to kill you. There was this African president, I don’t remember the name, he was saying we shouldn’t take the vaccine. (Focus group six)Amid rumors of the virus having been purposively manufactured by ‘man’, there were often whispers and propositions that the purpose of ‘concocting’ the virus was really to control a population boom. For instance, one mother cradling an infant worried that:There was a rumor saying they want to half the population of the [Black African] people. (Focus group one)Some of our participants genuinely believed there to be a deliberate effort to control Africa’s Black population by way of ‘testing’ a vaccine on it.There was also a rumor about the guy that funds [our hospital]—Bill Gates—he wants to test his vaccine on Black people only. (Focus group one)Once more, thinking about fields of suspicion towards inoculation and authorities, can there be any doubt by now what an amplification of this smoldering field might mean when fueled by scenarios in which White people underhandedly stage a viral genocide against Black Africans? The answer and outcome, of course, is vaccination hesitancy and surely a rise in expressed anxieties. This, however, is nothing new, and it is here worth noting certain semblances between the COVID-19 and HIV pandemics. Over four decades into the HIV pandemic, since its first reported case in 1981, misconceptions about the disease stubbornly persist to this day, as portrayed by Mwamwenda, including claims that:HIV is a disease of Black people; HIV was the creation of people who wanted to exterminate Black people, such as Africans, African Americans, and homosexuals; God sent HIV as a means of curbing or destroying sexual immorality; and HIV is transmitted through mosquitoes  would say: ‘it’s for people who travel abroad, so since they don’t go anywhere, they can’t have it’. It was meant for the wealthy people, the people that were dying, most were people from Italy. (Focus group six)Mkhukhu (shack) tenant in Soweto.Comparable claims have been reported from elsewhere in Africa, where views included that the virus was a “rich man’s disease” and cannot infect the poor . Aminu wAs we saw, the pandemic’s mediatization, at least as broadcast by health authorities and politicians, was placed within biomedical spheres of communicability. However, this approach “medicalizes politics because it sidesteps questions about the distribution of wealth and discrimination based on ‘race’” . The promuthi) and healers (sangomas)—cultural and spiritual practices endemic to the townships. Others, in turn, swear to faith healing or allopathic medicines. Several informants reported to have acquittances in their communities who repelled the idea of going to a hospital due to a public perception of hospitals as ‘risky places’.The stringent biomedicalization of COVID-19 and unilaterally clinical mediatization from health authorities may have augmented resistance towards those same authorities in the townships due to existing medical pluralisms in Soweto, mainly traditional phytomedicines Whereas most informants were not entirely impervious to the idea of visiting a hospital for COVID-19 treatment, they did, however, bemoan that hospitals would not provide them with indigenous remedies.At the hospital, they don’t allow umhlonyane  and garlic. The hospital just gives you hot water. (Focus group three)I believe it’s better to self-isolate at home because there it’ll be manageable. There was a rumor that if you drink artemisia [wormseed], it assists, so when you get to the public hospital—and that’s the place the government gives you to isolate—there’s no access to garlic and stuff. So, it’s better to be at home. (Focus group three)But they also make garlic water and lemon. There was a company where 32 of their employees had COVID. 11 of them got admitted to the hospital and they gave them these things; they all healed. (Focus group three)Artemisia has proven to be more practical and artemisia is more of a cultural thing. (Focus group three)In addition to traditional medicines, informants related that some people in the communities had come up with an alternative remedy to ward off COVID-19.My customers would say: ‘we sanitize with alcohol’ [laughs]. Yah! [It means that] you have sanitized the insides [giggles]. There’s another uncle, who says that if he drinks alcohol, the hot stuff, he won’t get sick, because the alcohol percentage is high, and if sanitizer also has alcohol, that means he’ll be fine. (Key informant one)We believe that the more alcohol you drink, the more this virus won’t get you. (Focus group one)Yes, I heard we should drink it [alcohol] from time to time because it helps. (Focus group three)My cousin is always with a Gordon’s [gin]. He encourages people to drink it because it helps the lungs. (Focus group three)Some appeared enticed and willing to resort to more extreme measures to neutralize the virus in what at times seemed like barely concealed substance abuse.Someone even said that we should drink the sanitizer. (Focus group two)In addition, informants oftentimes possessed indigenous knowledge about how to traditionally ameliorate certain symptoms that present like those of COVID-19, meaning that this disease may, however inadvertently, be treated as another condition altogether—and by traditional means. Again, nothing novel, some communities have long been known to believe that HIV can be cured by traditional healing. Mphahlele have also shown people to believe that COVID-19 is curable by means of traditional medicines listing indigenous herbs, such as eucalyptus-infused steam, which people have been using alongside alcohol as curative measures throughout the pandemic .Models of (bio)medical causation (aetiology) constitute ways of thinking about the world and interacting with it. Biomedicalized narratives about epidemics therefore “make racial and sexual inequalities seem natural as if bacteria and viruses gravitate toward populations and respect social boundaries” . As suchThough initially ascertaining peoples’ clinical well-awareness of COVID-19, in shook township communities, we descended into a maddening maze of false binaries—Africanized, racialized, and socially polarizing counterfactual claims built upon towering foundations of global conspiracy theories propagating everything in between a non-existence of COVID-19 and it being man-made—covertly designed to decimate populations and races. Others, due to medical pluralism and (resistance towards) the biomedicalization of the disease, simply turned to herbs and alcohol. This potent blend of facing life-threatening illness amid officially mandated limitations on freedom, nagging doubt about what is going on ‘behind the stage’, mixed up with covered-up social angst behind masks and daily casualty reports, transforms into and manifests as nervous hesitation to immunize. Counter- and nonfactual claims about COVID-19 may stem from misinformation, which then in turn results in variegated nuances of hesitancy, but we also discerned how these are truly expressions of resistance towards official doctrine and authority stemming from existing power inequalities that further aggravate societal polarizations during a pandemic. Growing fields of suspicion were fueled by fear and doubt, which, again, led to othering and its evil twin, racialization, the ‘Kung-Flu’. In Soweto, this was exacerbated by lacking access to formal media and (multiple) language barriers but, most importantly, by lingering site-specific structural violence brought on by legacies of colonialism and apartheid, fostering further resistance towards proponents of inoculation and officially blue-stamped health-related discourses. As such, understanding COVID-19 vaccine hesitancy in South Africa is—as much as it has been and still is the case with HIV—as much a matter of exploring its social aspects as it is of addressing its clinical dimensions, lessons which are best remembered during future outbreaks. It follows that it remains paramount during vaccination rollouts to unveil and address aspects detrimental to vaccine confidence and selectivity, especially in lower-income groups for underlying, context-specific cultural, spiritual, historical, and socioeconomic reasons. Appropriate mediazation alongside a debunking of counterfactual claims is crucial in driving forward immunization.


Self-employment (SE) is a growing precarious work arrangement internationally. In the current digital age, SE appears in configurations and contours that differ from the labor market of 50 years ago and is part of a ‘paradigm shift’ from manufacturing/managerial capitalism to entrepreneurial capitalism. Our purpose in this paper is to reflect on how a growing working population of self-employed people accesses social support systems when they are not working due to injury and sickness in the two comparable countries of Canada and Australia. We adopted ‘interpretive policy analysis’ as a methodological framework and searched a wide range of documents related to work disability policy and practice, including official data, legal and policy texts from both countries, and five prominent academic databases. Three major themes emerged from the policy review and analysis: (i) defining self-employment: contested views; (ii) the relationship between misclassification of SE and social security systems; (iii) existing social security systems for workers and self-employed workers: Ontario and NSW. Our comparative discussion leads us toward conclusions about what might need to be done to better protect self-employed workers in terms of reforming the existing social security systems for the countries. Because of similarities and differences in support available for SE’d workers in the two countries, our study provides insights into what might be required to move the different countries toward sustainable labour markets for their respective self-employed populations. Self-employment (SE) has emerged as a non-standard, precarious, and contingent work relationship internationally ,2,3. TheMounting international evidence stresses that precarious employment conditions are having profound adverse effects on workers’ safety, health, and wellbeing ,16,17. DOur purpose in this paper is to reflect on how a growing working population of SE’d people access welfare state social support systems when they are away from work due to injury and sickness in the two comparable countries of Canada and Australia- in terms of social, political, and cultural contexts. We take the cases of Canada and Australia, as both countries have key similarities in terms of comparable economies and liberal welfare states . As wellIn this paper, we make three distinct contributions. First, we explore how challenges to defining the status of a worker/SE are connected to accessibility to social supports in general and comparative analysis of two jurisdictions of Australia and Canada that recognize differences (similarities as well) in their social support policies and legal protocols. Second, we unpack the debates around the definitions, classifications, and misclassifications of SE, shedding light on differences between the two countries, convergence and divergence of different stakeholders’ views and perspectives, showing how they define, redefine, and reform the status of SE for the sake of their socio-political interest. Third, we make a snapshot of the social support systems available for workers for the said jurisdictions, where the status and position of SE’d workers are conspicuously designated, by analysing when SE’d workers are entitled to the available support systems, can opt out or opt into the supports; this analysis demonstrates the relative strengths and limitations, and gaps of the existing systems, which provides lessons for both jurisdictions for further policy formulation and reformations. Finally, the paper concludes with policy implications, as this study is prescriptive in nature, that is, it follows a method of analysis aimed at new policy ideas in order to improve the social welfare of SE’d workers in Canada and Australia . In the SE’d workers are generally depicted as a special group of homogenous people , who posA clear dark side of this SE labour market exists in that a significant number of SE’d workers are compelled to undertake this type of work due to unemployment, scarcity of alternatives, and everyday financial hardships ,5,12. AsIn addition to income-based poverty, a key challenge facing SE’d workers is what happens when they are unable to work due to illness or injury/disabilities, whether on a short- or long-term basis. This is also connected to poverty but in a different fashion. Some SE’d workers do not expect sick pay, paid annual leave, or a future pension because they are well-off and have adequate savings for the future . Some liGlobally, many policies and much legislation, such as workers’ compensation, employment insurance, and state pension plans, exclude SE’d workers. Indeed, Quinlan  noted thSpasova, et al.  illustraOverall, Spasova et al.’s analysis shows that although the welfare countries show comparatively comprehensive social protection for self-employed people in terms of the access to (basic) pension and (basic) health insurance, they still have social protection coverage gaps for SE’d in countries . To put Although a growing body of research sheds light on SE’d workers in terms of their health and well-being, social mobility, and racial and gender discrimination ,41,42,43We adopted ‘interpretive policy analysis’  as a metWe collected and analysed a range of secondary data related to work disability policy and practice in Canada and Australia. We focused on ‘work disability policy’, which is diverse policies connected to workers’ compensation, sickness and disability policy, and the legal and regulatory protocols and frameworks of social security . The seaThe final selected documents obtained from both searches-systematic and non-systematic were examined following Dixon-Woods and colleagues’ processes of quality assessment, data extraction, and data synthesis ,59. TheyPrevailing definitions and conceptualizations of SE are contested and vary, which reflects that there is not one type or state of SE. Additionally, the existing legal protocols, in Canada and Australia, dealing with employee and employment minimally defines SE, as is shown in the These multiple terms make SE challenging to define, both conceptually and empirically . AccordiThe self-employed include working owners of an incorporated business, farm or professional practice, or working owners of an unincorporated business, farm or professional practice. The latter group also includes self-employed workers who do not own a business (such as babysitters and newspaper carriers). Self-employed workers are further subdivided by those with or without paid help. Also included among the self-employed are unpaid family workers. They are persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling. They represented in 2011 about 1% of the self-employed. To put the analysis succinctly, Australia seems conservative in demarcating the multidimensional features of SE. Although it distinguished independent contractor from the business operator, it still remains unclear. However, Statistics Canada is liberal to fragments the SE, by clearly defining incorporated and unincorporated SE.Finally, we view, across all the national contexts and differences, the SE through a broader lens, as individuals who work for themselves instead of working for others like paid workers. Many may work alone, but others may have their own small business with or without employees. In this sense, there is an inevitable overlap between employers, self-employees, and employees. In short, SE is a diverse work arrangement, encompassing occupations ranging from highly paid professionals or billionaire entrepreneurs to low-skilled workers operating a business on their own.SE’d workers are often misclassified because employers seek to reduce legal commitments and compensation. The potential (mis)classification of workers in dependent employment relationships such as SE’d has been described by socio-legal scholars, as well as the European Commission and the International Labour Organization, due to the rising ‘gig’ economy in certain industries, such as construction industries ,93. Not Both Ontario and NSW have multiple mediums to support their citizens as well as workers in terms of government and non-government agencies by involving different stakeholders, such as hospital, ministries of governments, insurance boards and companies . GeneralIn Ontario, Canada, people, regardless of prior employment status, are entitled to get support from the Ontario Disability Support Program (ODSP), if they are 18 years and older, disabled and need support to meet living expenses, and their family income and assets are below a cut-off line. As such, eligibility is assessed both financially and medically. ODSP offers financial assistance to claimants and their family for essential living expenses, prescription drugs, vision care, help to find jobs and training to continue their jobs. Similarly, ‘Ontario Works’ provides financial and employment assistance to people, regardless of the nature of the jobs, who are 16 years and older, and in need of meeting basic living expenses for themselves or their family . They are provided with financial assistance, including income support to help with the costs of basic needs, health benefits for clients and their families, and employment assistance to help clients find, prepare for, and keep a job.In terms of Medicare, citizens and permanent residents in Ontario are entitled to the Ontario Health Insurance Plan (OHIP) in order to use medical facilities, cover appointments with doctors, hospital emergency rooms, medical tests and surgeries. Every Canadian citizen and permanent resident including their families, except people from Québec, which has its own plan, are entitled to have a Canada Pension Plan (CPP), covering partial replacement of earnings during retirement, disability or death. Benefits include a retirement pension, disability benefits, survivor’s pension, death benefits, and children’s benefits. To sum up, ODSP does not require contributions from workers, but it is means-tested, whereas OHIP and CPP are not means-tested. Workers have to contribute to a fund to be eligible for CPP, but not for OHIP.Regardless of place of injury either in the course of work or outside of work, in NSW, Australia, anyone can have access to supports from icare, self-insurance, and specialised insurance, which are managed/implemented by SIRA (State Insurance Regulatory Authority). These supports are provided to all Australian residents across social assistance and mandatory occupational pension systems, such as old age pension, disability pension, survivor’s pension. The social assistance (cash sickness benefits) and universal  systems cover sickness and maternity benefits, temporary disability benefits, permanent disability benefits, and workers’ medical benefits, and unemployment and family allowances, involving compulsory insurance with a public or private carrier under different schemes established and run by state and territory governments.However, people in NSW, including the SE’d, are required to pay a Compulsory Third Party Premium (CTP) when a vehicle is registered for motor accident insurance, which is managed by SIRA under a Compulsory Third Party (CTP) scheme, which covers injury involving motor vehicles. The benefits coverage of this scheme includes compensation for people who are killed or injured. Compensation can also include hospital, medical and rehabilitation costs, loss of earnings, and pain and suffering. Some aspects of compensation are reliant on establishing fault by another party and some are payable regardless of fault. The third-party insurance component of the scheme (CTP) is underwritten by five insurers. Insurer pricing and behaviour is monitored and regulated by SIRA. Finally, the National Disability Insurance Scheme (NDIS) is also a federal government funded program for disabled (irrespective of causes) people from 7 to 64 years old living in Australia with permanent and significant disability, and it may be the main supplier of benefits, or additional to other state funded supports. Overall, most government benefits are income-tested and asset-tested, implying that workers’ entitlements reduce as resources increase .In Ontario, an Employment Insurance special benefits (EI) exist, which SE’d workers in Ontario can opt into if they choose to register with CEIS (Canada Employment Insurance Commission). This provides benefits, one year after registering and paying monthly premiums, including maternity, parental, sickness, compassionate care, family caregiver for children, and family caregiver for adults . In this case, a SE’d worker who claims for compensation may receive up to 55% of his/her average weekly pay up to a maximum annual limit. However, if business revenue is generated during their leave, the funds are reduced accordingly . According to a report by the Canada Employment Insurance Commission (2014), SE’d women between the ages of 25 and 44 years old made 90.4% of all special benefits claims, mostly for maternity and parental benefits. According to Hilbrecht , there aIn Australia, including NSW, SE’d workers can opt into the work injury scheme if they voluntarily participate by paying premiums for self-insurance. This covers temporary and permanent disability benefits, and workers’ medical benefits as well as unemployment and family allowances. In addition, SE’d workers in NSW can buy personal injury/accident insurance, though it is not connected to CTP. In addition to other injury, it may cover insurer for injury in the event of a motor vehicle accident, regardless the fault. It may also cover gaps or limitations in the private health insurance shows.Generally, it is still challenging to define how many SE’d workers are under coverage of government and private supports because the existing evidence pertinent to SE and compensation regimes is scarce, conflicting, and partial . There iTo sum up, SE’d workers in NSW have more access to schemes based on voluntary participation than do these workers in Ontario. As such, supports are provided in Ontario irrespective of workers’ employment status, whereas some are means-tested  and some schemes requires contributions from the workers . Similarly, workers in NSW regardless of their employment status have also access to several types of social supports. Of these, some of the schemes expect contributions from the workers . However, Ontario has limited provisions including the EI special benefits program, which provides SE’d people with a significant number of benefits in return for paying a premium, though it fails to attract low-earning SE’d workers because they cannot afford it with the high rate of premiums. In terms of mandatory schemes, both jurisdictions have multiple alternatives, but each provide limited provisions for SE’d due largely to complicated eligibility criteria.Currently, key challenges with SE are in its definition, conceptualization, and classification. Mounting evidence shows that SE is often misclassified and mistakenly defined ,91,92,97To date, it seems that policies in Canada and Australia continue to visualize SE’d workers as the highly paid variety who may not need financial support when ill or injured. However, many studies have documented that this assertion about SE’d workers is an over-generalization and refers to a group of people who are financially prosperous, younger and highly educated, and who became SE’d for opportunity rather than necessity ,53,100. Our review reveals that both Ontario and NSW have limited social security provisions for SE’d workers when injured, ill or out of work . In OntaBoth jurisdictions, NSW and Ontario, have strengths and drawbacks in terms of support systems available for SE’d workers. On one hand, Ontario’s SE’d- focused special EI is comprehensive, and covers maternity, parental, sickness, compassionate care, family caregiver for children, and family caregiver for adults . On the other hand, most of NSW’s systems are narrow and constrained by multiple conditions. For example, NSW’s workers’ compensation and work injury covers only injury, not sickness or disease, and the injury needs to be caused by work. It is noteworthy that proving benefits for work-related injury for SE’d people is challenging because their working relationships and arrangements often blur, unlike those of many regular employees. For example, a SE’d person with a home office may have difficulty distinguishing a home-related versus a work-related accident. Another important difference between the two jurisdictions is that SE’d workers in NSW are entitled to apply for unemployment allowance, which is solely provided by government, whereas this is not possible for SE’d workers in Ontario. Similarly, SE’d people in NSW who have limited income can apply for sickness and maternity benefits, and family allowances. However, SE’d workers in NSW are excluded from other supports, such as old age pension, disability pension, survivor’s pension.SE’d workers can pay for private insurance with sickness and injury coverage in Ontario. However, when the WSIB imposed mandatory insurance on the SE’d construction workers in Ontario in 2013, they encountered protests from independent contractors who did not want to be required to pay this insurance premium that was more costly than what they had been paying for private insurance and that did not cover non-work-related illness and injury . In our (i)Although ‘Employment Insurance special benefits’ in Canada are not always used by SE’d workers in Canada due to the financial burden of premium payments, it nonetheless provides an example of a coverage system for SE’d workers that provides temporary income supports for parental, sickness or compassionate support leave etc. This is one way in which SE’d workers are recognized as a cohort. Hence, in the sense of equity, SE’d workers in NSW, Australia, might be treated in a similar manner, but after revisiting the issue of premiums.(ii)Basic income policies may be a solution to providing a basic social safety net to SE’d people, among others. An advantage of this approach is that it draws on the general tax fund rather than relying on taxing incomes of low-wage SE’d people, who are already income insecure ,112.(iii)All workers, whether SE’d or not, should be covered by workers’ compensation regimes. Digital platforms such as Uber should be required to pay into this scheme.(iv)For both jurisdictions, emergency income supports can be introduced for SE’d workers so that they can be supported when facing emergency circumstances, including but not limited to natural disaster, pandemic, injury/sickness. In this context, for example, COVID emergency benefits in Canada  was a successful program to address and protect SE’d workers.(v)Against the backdrop of a changing labour market in the digital age, SE is inevitable and obvious. A premise guiding policymaking is that SE’d workers should not be at a social security disadvantage relative to employees.(vi)Governments should create explicit policy to deal with SE’d and precarious workers to remove grey zones and clarify eligibility for compensation.(vii)As women and recent immigrants are more prone to be SE’d workers in recent years, childcare for the SE’d deserves special policy attention.(viii)Underreporting of compensation claims is a big issue for the labour market and social safety net policies. A strong social mobilization program may be required in order to reduce underreporting.(ix)A social supports literacy campaign may be introduced by both jurisdictions, using mass media or social media, because most of the SE’d workers in practice are not aware of the available supports systems to which they are entitled. However, there are still some support systems available for the SE’d workers in both jurisdictions.(x)In the case of both jurisdictions, SE’d workers, irrespective of the sector of work, platforms , structure of working relations (solo or paid employees), size of the business/professional clients  need to be given access to ‘collective bargaining’. These rights should be granted whenever necessary to prevent the contracting party with the dominant bargaining position from exercising a compression of labour standards . In thisRegardless of the segment of SE, be it independent contractor, entrepreneurship, small business, startup, unlike employees, the issue of supporting SE’d workers during injury and sickness is an ignored discourse in Canada and Australia. There is a gulf between how the number of SE’d workers are ballooning against the backdrop of the ‘gig’ economy and how these rising working populations lack attention in social security systems in Ontario and NSW. Policies in both jurisdictions appear to be based on the traditional picture of prosperous, well-organized SE’d workers not needing support from the state. However, this is an overgeneralization and a hyper-reality because at present tens of thousands of low paying SE’d workers strive to lead a decent life. Undoubtedly, they face very difficult circumstances when they have to be away from work due to injury or sickness, as this strata of the SE’d population generally cannot afford private insurance. In fact, at present, compensation for SE’d workers in both Ontario and NSW remains deceptive. Work is needed at both the policy and practice level to incorporate the voices of SE’d workers into compensation. Our comparative discussion leads us toward conclusions about what might need to be done to continue with unmasking the illusion of the traditional well-to-do self-employed worker:We are aware of a number of limitations of our study. First, we were dogged by the dearth of data around SE’d workers for both countries. It was a challenge to sort the data for SE’d workers from other precarious workers because most of the documents overlap these segments of employment. In short, we agree with several researchers that SE is poorly documented and understood. Second, in both Ontario and NSW, the labour market is undergoing rapid change and development at both policy and practice levels. Therefore, what we have written about in both places is not static; nevertheless, we argue that the broad themes emerging from our work will be relevant in both places for a significant time to come. Apart from the established databases for scholarly articles and documents, we relied on Google’s search engine to capture grey literature and ongoing government data. As the outcome of Google searches are filtered, we worked diligently to sort out the relevant documents. Despite these limitations, consideration of the issues that emerged from our description and analysis of identifying SE’d workers and compensation or supports for absence for work due to sickness and injury policy and practice in both countries will, we hope, support policy makers, people working in administering workers and compensations, researchers in their task of moving toward a sustainable compensation policy, and the imperative of tackling the gaps in the existing systems.