Bibliographic record
Abstract
Recent evidence suggests that recessions play a crucial role in promoting automation and the reallocation of productive resources. Consistent with this, I show that in the three previous Canadian recessions, routine jobs were disproportionately lost. COVID-19 is likely to have a similar impact, but bigger because superimposed onto the usual recessionary transformational forces are health-specific incentives to automate. Using O*NET data, I construct an index of COVID-19 health risk and of routine task intensity to measure health incentives to automate and the feasibility of doing so. Across occupations, income groups, industries, and regions, the two indices are strongly negatively correlated, suggesting that automation will not be overly focused and that it may penetrate into hitherto relatively unaffected sectors like health and education. Nevertheless, office and health support workers are likely to be disproportionately affected, as will the retail and hospitality industries. The impacts will also be primarily felt by families toward the bottom of the income distribution and in smaller cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".