Hard Work and You Can’t Get It: An International Comparative Analysis of Gender, Career Aspirations, and Preparedness Among Politics and International Relations PhD Students
Bibliographic record
Abstract
ABSTRACT Do all PhD students aspire to an academic career? Do PhD programs appropriately prepare students for the realities of the job market? There is a well-established gap between political science PhD graduates and tenure-track academic postings. The mismatch between PhD graduates and academic positions may point to alternative models of doctoral education as a possible solution. However, the survey of Canadian and Australian PhD students described in this article suggests that issues and challenges are common regardless of the model of doctoral education. Canadian PhDs report more mentoring activity, but they also are more fixated on securing academic positions. However, we find important gender differences across countries: men are more interested in an academic career and only a (disproportionately male) minority is confident that they will succeed in securing a faculty career. This raises questions about diversity in the future of the profession. This research suggests that although students have different experiences under different doctoral models, issues of academic jobs and a mismatch are common in both systems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".