Reflections on the CES Case Competition: The Coaches’ Perspective
Bibliographic record
Abstract
As described by Obrecht, Porteous, and Haddock (1998), the impetus for starting the inter-university Case Competition was the desire to generate student interest for both evaluation and the CES. Getting the Competition off the ground in its first year, however, took the enthusiasm of a part-time lecturer to inspire a colleague in another university. As we will describe, the hardest part is often getting started. Once an instructor and his or her students try the Competition, and especially if they reach the finals, there is no stopping. Returning students want to enter again to reach the finals, win, or defend their title, and they will strong-arm their classmates, new students, and coaches as necessary to make this happen. Preparation for the Competition and use of previous cases become incorporated into the evaluation training curriculum. In order to assist and, hopefully, inspire more instructors to get involved in the Case Competition, this article describes how we got started, how we involve and prepare our students, and the rewards of the competition experience for the students, coaches, and university itself.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.023 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 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".