Planning for the Future: Methodology Training in Canadian Universities
Bibliographic record
Abstract
Abstract Recent changes in government policy making and the labour market have created new opportunities for political scientists, provided that we have the skills to respond to them. We argue that changes need to be made in the area of methodology training in order to capitalize on these opportunities. Canadian political scientists should ensure that all our students acquire basic quantitative competencies, in addition to research design and qualitative analysis training, and that those graduate students interested in more sophisticated quantitative methods have the opportunity to develop those skills. We explain how expanding and deepening training in quantitative methods is one strategy for ensuring a role for political science in evidence-based policy making, for expanding labour market options for students, and for keeping apace with disciplinary trends. We caution, however, that special care needs to be taken to ensure that all political scientists have equal opportunities to develop such skills.
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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 | 0.155 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".