Gender, mood state, and justice preference: Do mood states moderate gender‐based norms of justice?
Bibliographic record
Abstract
The present study extends research on distributive justice by investigating whether a person's mood state moderates the robust effects of gender norms on allocation decisions. One hundred and eighty undergraduates (90 men: 90 women) were asked to undergo a mood induction procedure in which they were randomly assigned to a positive, negative, or neutral mood condition, and to work on a task with either a male or female co-worker (confederate). This resulted in a 2 (gender of participant) x 2 (gender of confederate) x 3 (positive vs. neutral vs. negative mood) between-subjects factorial design. Following completion of the task, participants were informed that they did 60% of the work and their co-worker did 40%. They were then asked to divide money between themselves and their co-worker in a way that they considered fair. The analysis revealed a three-way interaction in participants self-payment whereby men in a negative mood, working with other men took more pay for themselves than did participants in all other conditions. Specifically, 60% of the participants in this condition, allocated the payment either equitably or in a manner suggesting even greater self-interest. These results support the view that gender effects are strongly influenced by the presence of other relevant contextual cues.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".