What Social Supports Are Available to Self-Employed People When Ill or Injured? A Comparative Policy Analysis of Canada and Australia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".