The Effects of Rater Sex and Ratee Sex on Managerial Performance Evaluation
Bibliographic record
Abstract
Thirty-three men and twenty-four women evaluated and rated the performance of two managers (a man and a woman) after the managers had made decisions based on information provided by the management accounting system. The experimental materials comprised two cases—one showing successful performance and the other, unsuccessful performance. It is hypothesised that both men and women raters will demonstrate a bias against the successful woman manager, that is, evaluate her more harshly compared with the successful male manager. No difference in performance evaluation is predicted between the unsuccessful female manager and the unsuccessful male manager. The results support the hypotheses. A second dependent variable measured the perceived benefit that was received from expenditure incurred in carrying out the managers' decisions. It is hypothesised that both men and women raters will demonstrate a bias against the successful woman manager, that is, perceive lower benefit from a woman-initiated decision relative to a man-initiated decision. No evidence of biases against the successful woman manager is observed. The paper concludes with a discussion of the implications of these findings.
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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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".