The Health of the Classics Job Market during the Pandemic: A Long-Term Perspective
Bibliographic record
Abstract
The pressures of the COVID-19 pandemic have led to a 60% decline in the scale of the academic job market in classics in Canada and the United States. Review of online job posting data stretching back to the mid-1990s shows that the health of this market correlates closely with that of the economy at large. While minor year-to-year economic fluctuations have a minimal impact on the job market in the long term, recessions fundamentally alter its character, with the market remaining depressed for years after the economy itself has recovered. Compounding this problem is the oversupply of PhDs flooding the market at present, a consequence of the long training period of graduate school, which keeps PhD output high for many years after a wave of undergraduate enrolment peaks. A third factor is the trend in academia to short-term positions with high teaching loads, which leads to fewer openings for permanent jobs and a diminished need for faculty. Taken together, current trends bode ill for the future of our discipline and pose an existential threat for many smaller programs.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".