How Do the Labour Force Characteristics Encounter COVID-19 Economic Consequences—A Canadian Experience
Bibliographic record
Abstract
This paper draws on a current international analysis of pandemic consequences in the labour market and on the way different segments have been impacted. The purpose is to provide a critical investigation of the facts and arguments regarding how and why the consequences of the same health epidemic are differently faced at an uneven socio-economic burden. The objectives are twofold: First, we aim to explore on an international level the inequality settings that COVID-19 has highlighted, focusing on the most affected economic pillars such as the labour market. Second, we provide an empirical analysis of the likelihood of Canadian labour force participants to be unemployed before and after COVID-19, as one of the measurable effects of the pandemic. We assess how the likelihood of the working-age population falling into the unemployment pool varies before, during and immediately after the pandemic restrictions ease, using Canadian Labour Force Survey microdata. The findings indicate that mainly immigrants and youth suffered the most, pointing out their probably higher participation in precarious jobs and calling for policy initiatives to fix the structural faults in the labour market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".