Different Strokes for Different Folks: Gender and Emotions in an Environmental Game
Bibliographic record
Abstract
Females are often expected to behave more environmentally-friendly than males, to be more sensitive to nuances in wording/framing, to be more emotionally expressive, and to be more likely to act on these emotions. Do they actually behave according to these expectations? Previous research found mixed evidence on gender effects. The purpose of our study is to examine whether there are gender differences in reaction to framing, expressing of emotions and acting in response to emotions in the environmental context and to determine whether these differences (if present) follow the “stereotypical” expectations. To investigate this, we conducted a framed laboratory experiment in the water quality context. Our findings show that there is a gender effect and it is highly context-dependent with respect to environmental decisions and with respect to the likelihood of expression of positive and negative emotions. Furthermore, we find that females sharing behavior is not sensitive to empathy vs. self-interest framing, while males sharing behavior is. Our results indicate that the payoff-relevant factors are, generally, more important than gender. We conclude that males and females are responding to different stimuli (“different strokes for different folks”), thus empirically testing behavior in a specific context is paramount when trying to predict responses by gender and designing environmental policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".