Developing Evaluation Capacity Building Competencies: Participant Reflections From the Evaluation Capacity Case Challenge
Bibliographic record
Abstract
In 2023, McGill University’s Max Bell School of Public Policy hosted the inaugural Evaluation Capacity Case Challenge (EC 3 ) competition with a cohort of 19 selected graduate students and early-career professionals studying or working in Canada or the United States. It was a multifaceted learning opportunity for participants to expand evaluation capacity building (ECB) competencies and served as a bridge between formal education and real-world practice. This practice note offers reflections from five students representing all teams and one coach on how EC 3 supported competency development as outlined by the Canadian Evaluation Society and the American Evaluation Association. Focused on domains related to professional reflection, technical and methodological skills, situational context, as well as management and interpersonal skills, this article explores the role of EC 3 in honing skills specific to ECB, preparing evaluators to excel in their roles and champion ECB in diverse and evolving contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".