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 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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".