From it’s “It’s Hell Out There” to being one of the “Lucky Ones”: The Trends and Tales of the Canadian Psychology Academic Job Market from 2012-2022
Bibliographic record
Abstract
There is a perception among those on the Canadian Psychology academic job market, that hiring expectations (e.g., number of publications, grants, accomplishments) have increased dramatically over the past decade. However, no data on hiring expectations across all areas of Psychology is available to inform career planning decisions. The purpose of this study was to understand the current psychology academic hiring experience through a mixed-methods approach. Focusing on faculty members hired from 2012-2022/3, data was collected via 1) an online search of Canadian Psychology departments (Study 1: N = 439) and 2) an online survey (Study 2: N = 76). Study 1. On average, excluding those hired into teaching positions, candidates were on the job market for M=4.05 years and had M=20.25 publications upon hire. These numbers varied depending on the year, gender, and area of research. There was a 24% increase in the number of publications between those hired in 2012-2016 versus 2017-2022. Universities with medical schools were more likely to hire candidates trained in the US compared to comprehensive or undergraduate universities. Study 2. In a smaller sample of self-reporting faculty members, research-stream professors took an average of 2.34 years to obtain their first position and reported an average of 15.12 (SD=15.13) total publications upon hire. Thematic analysis of open-ended responses identified the following themes: 1) frustration and hopelessness, 2) location and moving barriers, 3) feelings of “luck,” and 4) high standards in the field. Findings will inform current job market expectations and guide students toward successful career choices.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".