Measuring Rank-Based Utility in Contests: The Effect of Disclosure Schemes
Bibliographic record
Abstract
This article studies how the incentive structures and disclosure schemes of a contest affect the contestants’ intrinsic motivations. Specifically, the authors measure the effects of these design decisions on two types of nonmonetary rank-based utility: self-generated and peer-induced. They run a set of laboratory experiments involving contests under various reward spreads and disclosure schemes. First, they find that virtually all commonly adopted disclosure schemes generate positive peer-induced rank-based utility. However, the relative performances of alternative disclosure schemes can depend on the spread of contest rewards and the number of contestants. Second, being recognized as a winner confers positive peer-induced rank-based utility; moreover, being recognized as the sole first-place winner or as one among multiple winners does not produce significantly different peer-induced utility. Third, “shaming” by disclosing the identity of contestants ranked at the bottom leads to negative peer-induced rank-based utility, but the effect is marginally insignificant. Finally, a smaller spread of contest rewards consistently results in higher levels of self-generated rank-based utility. These results underscore the importance of jointly choosing incentive structures and disclosure schemes.
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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 | 0.062 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".