What Is a Good Rank? The Effort and Performance Effects of Adding Performance Category Labels to Relative Performance Information*
Bibliographic record
Abstract
ABSTRACT Prior research demonstrates that relative performance information affects effort and performance. However, little is known about the qualitative design parameters of these information systems. This study examines, via an experiment, how adding performance category labels to ranks (e.g., “good” ranking position and “poor” ranking position) affects effort and performance. Furthermore, we investigate the effort and performance effects of two design choices observed in practice: the type of performance category labels and the proportion of positively labeled ranks. We argue that performance category labels motivate greater effort and performance through competition for status, which varies with both the type of performance category labels and the proportion of positively labeled ranks. We find partial support for our hypothesis that adding performance category labels increases effort and performance. Specifically, we find positive effects if top ranks are positively labeled and bottom ranks are negatively labeled (combined labels) but not if only top ranks are labeled (positive‐only labels). We also find as predicted that the positive effects on effort resulting from using combined labels, instead of positive‐only labels, are stronger when the proportion of positively labeled ranks is larger. The results for performance are weaker. Our results shed new light on the usefulness of performance category labels and emphasize how firms can render relative performance information more effective.
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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 | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".