A comparative study of governmental financial support and resilience of self-employed people in Sweden and Canada during the COVID-19 pandemic
Bibliographic record
Abstract
Globally, self-employed people were among the hardest hit by the repercussions of the COVID-19 pandemic and faced hardships such as financial decline, restrictions, and business closures. A plethora of financial support measures were rolled out worldwide to support them, but there is a lack of research looking at the effect of the policy measures on self-employed people. To understand how different governmental financial support measures enhanced the resilience of the self-employed and improved their ability to manage the pandemic, we conducted a mixed-method study using policy analysis and semi-structured interviews. The documents described policies addressing governmental financial support in Sweden and Canada during the pandemic, and the interviews were conducted with Swedish and Canadian self-employed people to explore how they experienced the support measures in relation to their resilience. The key results were that self-employed people in both countries who were unable to telework were less resilient during the pandemic due to financial problems, restrictions, and lockdowns. The interviews revealed that many self-employed people in hard-hit industries were dissatisfied with the support measures and found them to be unfairly distributed. In addition, the self-employed people experiencing difficulties running their businesses reported reduced well-being, negatively affecting their business survival.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".