Youth unemployment in Canada, Germany, Ireland, and the United Kingdom in times of COVID-19
Bibliographic record
Abstract
From the start of the COVID-19 pandemic, there were widespread concerns about young people’s labour market prospects. The COVID-19 youth economic activity and health monitorNote (YEAH) project at University College London (UCL) in collaboration with Statistics Canada and other institutes in Europe aimed to shed light in this area by examining the pandemic’s impacts on the dynamics of youth employment and well-being. Indeed, very few countries managed to avoid a hit to their economy or young people’s employment in the wake of COVID-19. The economic fallout from the pandemic has been as global as the health crisis itself. This spotlight article shows that despite economic and institutional differencesNote, youth unemploymentNote figures in Canada, Germany, Ireland, and the United Kingdom (UK) initially rose during the COVID-19 pandemic with peak levels in the summer of 2020, but have recovered since then.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".