When Managers Become Robin Hoods: A Mixed Method Investigation
Bibliographic record
Abstract
When subordinates have suffered an unfairness, managers sometimes try to compensate them by allocating something extra that belongs to the organization. These reactions, which we label asmanagerial Robin Hood behaviors, are undertaken without the consent of senior leadership. In four studies, we present and test a theory of managerial Robin Hoodism. In study 1, we found that managers themselves reported engaging in Robin Hoodism for various reasons, including a moral concern with restoring justice. Study 2 results suggested that managerial Robin Hoodism is more likely to occur when the justice violations involve distributive and interpersonal justice rather than procedural justice violations. In studies 3 and 4, when moral identity (trait or primed) was low, both distributive and interpersonal justice violations showed similar relationships to managerial Robin Hoodism. However, when moral identity was high, interpersonal justice violations showed a strong relationship to managerial Robin Hoodism regardless of the level of distributive justice.
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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 | 0.039 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".