Teaching policy capacity: A collaborative and constructivist workshop
Bibliographic record
Abstract
This article presents an approach for teaching policy capacity to civil servants based on a workshop that took place in 2018 under the auspices of the Government of Prince Edward Island’s Policy Capacity Learning Series. It argues that workshops which introduce civil servants to the concept of policy capacity can enhance skills-based training and knowledge of the policy environment. Through a learner-focused, collaborative and constructivist pedagogy, the workshop involved a group activity where civil servants constructed a visual diagram of their policy environment by categorizing actors, skills, resources, institutions and concepts according to a policy capacity framework. This article discusses the workshop’s planning and delivery requirements which can be used, adapted and improved by practitioners in other jurisdictions. It also provides considerations for future training and education in public administration.
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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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".