Strengthening Evaluation Capacity Building Practice Through Competition: The Max Bell School of Public Policy’s Evaluation Capacity Case Challenge
Bibliographic record
Abstract
Despite the attention evaluation capacity building (ECB) has received over the past several decades, surprisingly few opportunities for learning about ECB exist. In response to this need, the Max Bell School of Public Policy at McGill University, in collaboration with ECB scholars across Canada, created a case competition focused exclusively on ECB—the Evaluation Capacity Case Challenge (EC 3 ). Twenty individuals interested in learning about ECB and one organization (case site) interested in enhancing their existing evaluation capacity were selected to participate through a competitive application process. Participants attended a series of online workshops and engaged with an ECB coach to hone their skills and then took part in a two-day hybrid case challenge event where they had 24 hours to craft an ECB plan in response to a specific case challenge question presented by case site representatives. In this article, the authors describe EC 3 in detail and share some key reflections from the inaugural year.
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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 | 0.076 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".