Are Canadian‐Trained PhDs Disadvantaged in the Academic Labor Market?
Bibliographic record
Abstract
Beaucoup d’étudiants s'inquiéent de leurs perspectives d'emploi à la fin de leurs éudes. Un de leurs soucis principaux provient de l'idée répandue chez les Canadiens qu'une formation doctorale outre‐mer augmente leurs chances de trouver une position à leur retour au pays. Ils s'appuient sur la conviction que beaucoup de facultés de sociologie préfèrent engager des diplômés de programmes de pays étrangers. Or, l'information accessible concernant le nombre et l'origine des titres de professeurs en sociologie récemment embauchés au Canada, et compte tenu d'une attention particulière portée à cet écart, montre que les facultés canadiennes ont tendance à recruter plus de personnel ayant obtenu un doctorat au Canada qu’à l’étranger, bien qu'il y ait quelques exceptions. Many PhD students are anxious about their job prospects upon graduation. One of the major themes involves a perception that Canadians going abroad for doctoral training are privileged in their job search when they return to Canada to look for academic work. The belief is that many sociology departments prefer to hire graduates of programs outside the country. An examination of available information regarding the number and origin of degrees for recently hired sociology professors in Canada can help address this gap and finds that Canadian departments tend to hire Canadian‐trained PhDs more than not, but there are some exceptions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".