The signals we give: Performance feedback, gender, and competition
Bibliographic record
Abstract
Feedback is vital for growth and learning, yet anecdotal evidence suggests people often hesitate to provide it, and its provision may be shaped by asymmetries and gender-related biases. We study feedback provision across variations in the nature of performance signals, their instrumental value, and the recipient’s gender. We find that a surprising degree of both positive and negative feedback is withheld, with a follow-up experiment suggesting that advisors’ feedback decisions are driven mainly by transparency- and duty-related considerations, or, to a lesser extent, are motivated by self-serving reasons. Additionally, when initial performance signals are vague, advisors are more likely to withhold noninstrumental negative than positive feedback—an effect we conjecture may be stemming from the lower psychological cost of lying (by omission) under uncertainty. Suggestive evidence shows the difference is more pronounced for female recipients, and exploratory analysis traces this to stronger ego-protective concern by advisors for women than for men. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This study was funded by the British Academy [Grant SRG1920/100428], the Portuguese Foundation for Science and Technology [FCT Grant PTDC/EGE-ECO/5020/2021], the University of Exeter Business School, and the Faculty of Business and Law, University of Portsmouth. The study was preregistered on the American Economic Association’s registry for randomized controlled trials (No. AEARCTR-0006966), and it was approved by the Scientific Council of Nova School of Business and Economics and the Research Ethics Committees of both the University of East Anglia (0347) and the University of Exeter (8317399). Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.05001 .
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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 | 0.006 | 0.040 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".