Bibliographic record
Abstract
I consider a two-stage elimination contest with uninformed and informed players. Informed players can signal their type to future uninformed opponents through their efforts in the first stage. Relative to the benchmark case of complete information, I find that an informed player exerts a higher effort in stage 1, if the uninformed future opponent is weaker than him. Conversely, he exerts a lower effort, if the uninformed opponent is stronger than him. This result is consistent with a conjecture in Rosen (AER, 1986). Intuitively, informed players may want to scare future uninformed opponents by exerting higher efforts in earlier rounds. However, trying to scare a stronger player may not be a sensible strategy because he might compete very fiercely. In equilibrium, informed players who are stronger than uninformed players separate from informed players who are weaker than uninformed players. This result differs from Horner and Sahuguet (2003) where stronger informed players pool with weaker informed players.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".