Meeting the Challenge: How the City of Kingston Is Working to Propel Evaluation Growth
Bibliographic record
Abstract
This practice note shares key learnings from the inaugural Evaluation Capacity Case Challenge (EC 3 ) held by the Max Bell School of Public Policy in April 2023. The purpose is to give readers an opportunity to consider how to shape evaluation capacity in general and within a municipal context. Evaluation capacity building (ECB) is a multifaceted concept to support shared learning about and understanding of evaluation. Strands of learning about ECB emerged from EC 3 that can be applied within organizations. These centre on three important actions: (1) establishing a community of practice led by champions, (2) scaling and sequencing evaluation capacity, and (3) developing data literacy as a technical skill. The ECB strategies developed in EC 3 offered new ways for the City of Kingston to consider and reconsider how it conducts, shares, learns from, and uses evaluation. Here, the authors describe evaluation within the City of Kingston, the city’s goals in entering EC 3 , key learnings from the challenge, and the city’s ensuing efforts to propel the growth of evaluation.
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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 | 0.036 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.036 |
| Scholarly communication | 0.043 | 0.013 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".