Designing programs to prepare future faculty for academic careers: Insights from a longitudinal case study of a multidisciplinary cohort-based program model for doctoral students
Bibliographic record
Abstract
Many universities offer some version of centrally offered professional development opportunities for graduate students seeking academic careers. Less is known about what impact these programs have on student career preparation and success and which design elements are most beneficial to each learner (Diggs et al., 2017; Schram et al., 2017). This article reports on a mixed methods decadal review (2011–2021) of one large, research-intensive institution’s multidisciplinary cohort-based year-long program, Preparing for Academic Careers, for graduate students near the end of their doctoral or master’s of fine arts (MFA) degree. Results from a systematic employment status search using publicly available records (Google and LinkedIn) indicate that a higher percentage of participants are employed in academic positions than national trends. Results from the analyses of closed and open-ended questions from an alumni survey suggest a range of perceived benefits: an increased sense of belonging in the academy, comfort talking to others about their work, confidence as an instructor, and interest in cross-disciplinary work. These findings will inform others seeking to design and implement academic career preparation programs that aim to provide student-level support in an inclusive and multidisciplinary environment.
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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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".