Academic Inbreeding at the Canadian Engineering Schools
Bibliographic record
Abstract
Academic inbreeding is a label used when units, departments and universities hire their former students, predominantly their former Ph.D. students, as faculty members, a practice that is generally perceived as detrimental to academic productivity and diversity. In this paper, for the first time, we attempt to verify some of the reported attributes for a small sample of Canadian universities, namely for the largest engineering schools. We examined more than 60 departments and units at 11 universities. We show that academic inbreeding is in fact present at the investigated units with a national average of 23%. Twelve departments exhibited a Z-score of one (inbreeding index larger than 34%, four departments showed a Z-score score of almost 2 and higher (inbreeding index larger than 44%). As well, we demonstrate that the quality of publications, measured by the number citations, appears to be lower for the inbreds. We also introduce a new measure that seems to be more suitable to capture the negative effect of inbreeding on diversity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".