Bibliographic record
Abstract
We report an experimental test of alternative rules in innovation contests when success may not be feasible and contestants may learn from each other. Following Halac, Kartik, and Liu (in press), the contest designer can vary the prize allocation rule from Winner‐Take‐All (WTA) in which the first successful innovator receives the entire prize to Shared in which all successful innovators during the contest duration share in the prize. The designer can also vary the information disclosure policy from Public in which at each period, all information about contestants' past successes and failures is publicly available, to Private, in which contestants only know their own histories. In our setting, the optimal contest design in terms of maximizing the probability that at least one innovator is successful depends on the probability of successful innovation, given that innovation is feasible. Under some parameters the designer will prefer a WTA‐Public contest; while, under others he will prefer Shared‐Private. Our experiments provide evidence that Private disclosure contests behaviorally dominate Public disclosure, regardless of the prize allocation rule, and moreover that Shared‐Private contests dominate WTA‐Private contests.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".