Has Simeon's Vision Prevailed among Canadian Policy Scholars?
Bibliographic record
Abstract
Abstract Concerned by the proliferation of idiosyncratic prescriptive case studies in the nascent subfield of policy studies, Richard Simeon, in his seminal 1976 article, asked scholars to produce more comparative policy research that aimed at explaining general events and contributing to theory building. The extent to which Simeon's vision materialized remains debated. With a view to informing this debate, we conducted a systematic content analysis of the articles published in five major generalist public policy journals from 1980 to 2015. The analysis reveals that Canadian policy scholars took a comparative turn, publishing more territorial, sector and time comparisons than in the past. We also found evidence that theoretical knowledge accumulation is more important today for Canadian authors than it was when Simeon wrote his article.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.034 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".