Planning for the Future: Methodology Training in Canadian Universities
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
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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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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