The relation of climate change denial with benevolent and hostile sexism
Bibliographic record
Abstract
Abstract Ecofeminism purports that sexist ideology and climate change denial are related, as prejudice, and its desire for power and nonuniversalism, create a disregard for both. In three studies we examined the associations between hostile and benevolent sexism with climate change denial. The first study (n = 270) demonstrated that hostile sexism explained unique variance in climate change denial over and above two strong predictors of prejudice and climate change denial, social dominance orientation and right‐wing authoritarianism. The second study (n = 294) reports on the significant indirect effects of willingness to make sacrifices for the environment on the relation between hostile/benevolent sexism and climate change denial. The third and final study (n = 514) found significant indirect effects of hostile and benevolent sexism, as well as willingness to make sacrifices for the environment, on the relation between power and climate change denial. Universalism demonstrated direct effects with climate change denial when benevolent sexism and willingness to make sacrifices for the environment were taken into account; direct and indirect effects were found when hostile sexism and willingness to make sacrifices for the environment were considered. Our findings provide support for a strong relation between hostile sexism and climate change denial and suggest underlying psychological processes that may represent paths through which climate change attitudes could be indirectly modified.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".