An analysis of the Canadian cognitive psychology job market (2006-2016)
Bibliographic record
Abstract
How accomplished does one need to be in order to be competitive on the Canadian cognitive psychology job market? We looked at the publication record of everyone who was hired as an Assistant Professor in Canadian cognitive psychology divisions with PhD programs between 2006 and 2016 (N = 64). Individuals who were hired from 2006-2011 averaged 10 journal article publications up to and including the year that they were hired. However, this increased by 57% to 18 publications by 2012-2016. Notably, this increase (a) occurred despite an increase in the number of positions since 2010, (b) was not restricted to top-ranked institutions, (c) did not come at the cost of decreasing quality in research (based on citations), and (d) was not driven by longer postdoctoral fellowships. To supply context, we obtained data on the publication records of 98 eminent and early career award winning cognitive psychologists when they obtained their first faculty position. The correlation between year of hire and publication number in the full sample was strongly positive (r = .47) and driven primarily by a substantial increase in recent years. This suggests that the increasingly competitive job market is not specific to Canada. Finally, we found that behavioural (as opposed to neuroscience) researchers and those who obtained their PhD from Canadian universities may be at particular risk in the job market. At a time when increasing numbers of PhDs are graduating from cognitive psychology programs, it has likely never been more difficult to obtain a faculty position.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.051 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".