An assessment of the impact of COVID-19 on job and skills demand using online job vacancy data
Bibliographic record
Abstract
This policy brief uses online job vacancy postings as a partial indicator of the impact of COVID-19 on skills demand in five OECD countries (Australia, Canada, New Zealand, the United Kingdom and the United States) between January and November 2020. The pandemic, as well as containment and mitigation measures designed to halt its spread, had a large but heterogeneous impact on the demand for skills. By early May, the total volume of online job vacancies had fallen by over 50% in all the countries analysed with respect to the beginning of the year, with even larger declines in some sectors. However, the demand for specific skills in the healthcare sector and in logistics increased. There is also evidence of an increase in vacancies involving remote-working arrangements. The brief also shows that the crisis affected differently individuals with different levels of educational qualifications and that such effect differed across the countries analysed.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".