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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it