Ambivalent sexism and relationship adjustment among young adult couples: An actor-partner interdependence model
Bibliographic record
Abstract
This study examined the associations between ambivalent sexism (i.e., hostile and benevolent sexism) and relationship adjustment in young adult couples by testing an actor-partner interdependence model. The sample was composed of 219 cohabiting heterosexual Canadian couples. The findings suggest that ambivalent sexism plays a role in young adults’ perceptions of the quality of their romantic relationship, but gender differences exist. Women and men who more strongly endorsed hostile sexism tended to report lower relationship adjustment. Women’s hostile sexism was also negatively related to their partners’ relationship adjustment, whereas their benevolent sexism was positively related to their own and their partners’ relationship adjustment. For their part, men’s ambivalent sexism was unrelated to their partners’ relationship adjustment and their benevolent sexism was also unrelated to their own relationship adjustment. The results are discussed in light of the insidious consequences that can accompany ambivalent sexism. Even though hostile sexism functions to protect men’s societal advantages, it comes with costs to their romantic relationships. In contrast, despite the rewards benevolent sexism can bring on the relational level, its endorsement may hinder the attainment of gender equality by encouraging women to invest in their relationship at the expense of independent achievements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".