The CES Case Competition: A Valuable Resource for Community-Based Agencies
Bibliographic record
Abstract
The Annual Student Evaluation Case Competition of the Canadian Evaluation Society (CES) has gained widespread interest since its inception in 1996, growing from 2 participating teams in 1996 to 15 teams in 2002 representing several regions and academic disciplines in Canada. The primary goals of the Student Evaluation Case Competition were originally to provide students with a challenging learning experience to promote professional development for entrants to the evaluation field and raise the profile of evaluation within academic institutions. However, it soon became apparent that the competition had far-reaching benefits, including assisting underfunded agencies and organizations with evaluation needs. This article will illustrate the contribution that the Student Case Competition can make to agencies with evaluation needs by describing the experience of Rideauwood Addiction and Family Services (Ottawa, Ontario) whose Gambling Treatment Program was used as the case in the qualifying round of the 1998 competition.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.022 |
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".