“It’s as Long as a Piece of String!” Definitions of Canadian U15 Faculty at Midcareer
Bibliographic record
Abstract
The purpose of this study was to gain a better understanding of how midcareer should be defined in academia.To determine when academics enter and exit midcareer, we sent out an open-ended survey to deans, directors, and chairs in research-based universities across Canada (N=242).The survey findings revealed an understanding that the midcareer phase starts immediately or shortly following the tenure award (and, usually, promotion to associate professor), and one stays in the midcareer phase past the promotion to full professor.Our survey data also showed good agreement that one exits midcareer when one winds down research or achieves a notable recognition for careerlong work.Within midcareer, most respondents agree that there is an expectation for enhancement of one's research and service profile and to lead formally and informally in the university, the scholarly profession, and the community.Although increase in research output is an expectation, in the domain of research, there is a requirement that faculty recruit, retain, and mentor upcoming researchers (graduate students, postdoctoral fellows, and junior faculty).These achievements are not measured finely but constitute important milestones in the midcareer phase.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".