The Mock Academic Faculty Position Competition: A Pilot Professional and Career Development Opportunity for Postdoctoral Fellows
Bibliographic record
Abstract
PROBLEM: Medical educators face a dilemma in countries like Canada, where policy makers and strategic planners have prioritized highly qualified personnel and expanded recruitment of advanced trainees at a time when early-career specialists face prolonged job insecurity as they transition to professional employment. The University of Calgary Cumming School of Medicine hatched the Mock Academic Faculty Position competition to test the school's existing capacity to address the pressing career development needs of highly trained graduates. APPROACH: The competition was piloted in May-June 2014. Approximately 180 postdoctoral fellows were invited to compete; 34 submitted portfolios. The Postdoctoral Program Office established a longlist of 12 applicants. Through reviews, a selection committee identified 3 finalists to participate in a daylong event consisting of a research presentation and committee interview. The event was followed by approximately 70 audience members at any given time who were invited to complete anonymous evaluation forms and/or exit interviews. OUTCOMES: The selection committee deduced a vast majority of applicants did not sell their skills effectively or demonstrate research programs independent from supervisors. Exit interviews conducted with 40 audience members indicated 36 (90%) picked the same finalist as the selection committee, 34 (85%) found the process "nerve racking," and 28 (70%) had no previous idea of what goes on inside an academic committee interview. NEXT STEPS: A key recommendation for future iterations is early attention to systematizing feedback to ensure more direct impact for nonfinalists. Alternative initiatives for those gearing up for industry or public-sector work are being prepared.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".