Bibliographic record
Abstract
The outbreak of COVID-19 and the unprecedented measures taken by many countries to slow down the spread of the coronavirus caused large economic and psychological costs. This paper uses real time survey data from two waves run at the end of March and in mid-April to provide a snapshot of the actual labour market outcomes in twelve countries. Our study reveals large cross-country differences. At the end of March, when large disparity existed in the diffusion of the pandemic and in the lockdown measures, a large share of employed individuals had stopped working in France (38%) and Italy (47%), but much less in Australia (13%) and the US (10%). Large differences remained in mid-April. Yet, some common patterns emerge. Labour market outcomes varied according to workers’ educational attainments and occupation types. College graduates and white collars worked more from home and less from the regular workplace. Instead, low educated workers and blue collars were more likely to remain in the regular work place or to stop working. Similar patterns emerge with respect to the workers’ (family) income. This evidence suggests that initial labour market effects of COVID-19 (and of the lockdown measures) may have contributed to increase pre-existing inequalities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".