Teaching “Pays the Bills”? A Study of Doctoral Program Descriptions in Canadian Political Science Departments
Bibliographic record
Abstract
Teaching is an important practice in academic life, yet little scholarly research has explored the ways in which graduate education programs prepare the potential next generation of professors for this practice. This study explores the presentation of pedagogical training in the context of doctoral program descriptions offered by Canadian political science departments. By paying careful attention to the public-facing presentation of program descriptions, we can observe both the pedagogical training efforts that departments present as important for the graduate school experience as well as the different ways in which departments express the instrumental and/or intrinsic value of pedagogical practice. We find that many program descriptions present teaching roles in terms of providing financial support to graduate students, indicating an instrumental value. Others present preparedness to teach as an indirect benefit of the comprehensive examination experience. We highlight case studies of program descriptions that prioritize teaching and offer recommendations.
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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 | 0.029 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.029 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".