Training for policy capacity: A practitioner’s reflection on an in-house intervention for civil servants, students, and post-secondary graduates in Canada
Bibliographic record
Abstract
A substantial amount of scholarly work focuses on conceptualizing, theorizing and studying the policy capacity of governments. Yet, guidance for practitioners on developing policy capacity training programs is lacking. In this article, I reflect on my experience as a public servant in the provincial government of Prince Edward Island where I designed and implemented the Policy Capacity Development and Mentorship Program for civil servants, recent graduates and students. In this article, I offer a descriptive overview of the framework and logic of the program and discuss how I integrated policy capacity theory. This article may serve other practitioners who seek to implement similar programs in their respective organizations and provides a base for future interventions. The article also offers thoughts on practitioner-led collaboration with academics and recommendations for those who would like to establish similar programs in their organizations.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".