How Do Municipal Policy- and Decision-Makers Evaluate the Impact of Their Policies? Insights from the City of Regina in Saskatchewan, Canada
Bibliographic record
Abstract
In the public sector, evaluation is essential for assessing policy impact, implementation fidelity, and ensuring accountability among policy- and decision-makers. However, little is known about the extent to which evaluation is used to guide policy-making in small-to-medium-sized municipalities. This analysis sought to explore how municipal policy- and decision-makers in the City of Regina view and incorporate evaluation in their work. This qualitative case study used semi-structured interviews with 30 municipal policy- and decision-makers, and analyzed the data using thematic analysis. Our findings suggest that while municipal policy- and decision-makers have attempted to steer the policy development process toward democratic principles, enhanced legitimacy, and evidence-driven practices, there remain substantial barriers to implementing these changes. Key challenges, such as superficial legitimacy and limited resources, were found as obstacles to establishing a comprehensive evaluation framework that support consistent, transparent, and accountable municipal policy-making. Our findings provide valuable insights for those involved in the municipal policy-making process.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".