Departmental Engagement in Doctoral Professional Development: Lessons from Political Science
Bibliographic record
Abstract
There is widespread discussion about the need to develop and enhance the career prospects of PhD graduates, and many Canadian universities are seeking to provide professional development programs and mentorship specifically for doctoral students. This paper considers doctoral career preparation from the department level through an in-depth examination of how Canadian political science departments approach the issue, drawing on a survey of department chairs. We find that departments are supportive of professional development; while departments are not in the position to provide extensive programs and struggle to integrate efforts systematically, they are well-positioned to participate in collaborative approaches and welcome improved communication and coordination. We argue that graduate faculties should consult with departments and engage them in professional development program design, perhaps tailoring to specific disciplines as needed, and that departments should look for opportunities to work with graduate faculties before initiating their own programs.
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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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.038 | 0.020 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".