The U.S. faculty job market survives the SARS-CoV-2 global pandemic
Bibliographic record
Abstract
Abstract Purpose This paper aims to identify the extent to which the COVID-19 pandemic disrupted the academic job market and the ways in which faculty job applicants altered their applications in response to a changing academia. Design/methodology/approach The data presented here is the portion relevant to COVID-19 collected in a survey of faculty job applicants at the end of the 2019-2020 job cycle in North America (spring 2020). An additional “mid-pandemic” survey was used in fall 2020 for applicants participating in the following job search cycle to inquire about how they were adapting their application materials. A portion of data from the 2020-2022 job cycle surveys was used to represent the “late-pandemic”. Job posting data from the Higher Education Recruitment Consortium (HERC) is also used to study job availability. Findings Examination of faculty job postings from 2018 through 2022 found that while they decreased in 2020, the market recovered in 2021 and beyond. While the market recovered, approximately 10% of the faculty job offers reported by 2019–20 survey respondents were rescinded. Respondents also reported altering their application documents in response to the pandemic as well as delaying or even abandoning their faculty job search. Originality This paper provides a longitudinal perspective with quantitative data on how the academic job market changed through the major events of the COVID-19 pandemic in North America, a subject of intense discussion and stress, particularly amongst early career researchers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".