New Sexism in Couple Therapy: A Discursive Analysis
Bibliographic record
Abstract
The persistence of gender inequality in postindustrial societies is puzzling in light of a plethora of changes that destabilize it, including shifts in economy, legislation, and the proliferation of feminist politics. In family relations, such persistence manifests as a disconnect between couples aspiring to be more egalitarian yet continuing to enact traditional gender roles and hierarchies. There is an emerging consensus that gender inequality persists because of people's continued reliance on sexist ideology or gendered assumptions that constitute women as innately distinct from and inferior to men. Sexist ideology changes its form to accommodate to changing socio-economic conditions. Contemporary forms of sexism are old ways of legitimizing male power articulated in new and creative ways, often by incorporating feminist arguments. To effectively recognize and address "new sexism," scholars and practitioners require new, innovative research frameworks. Our objective in writing this article is two-fold. First, we seek to advance discursive (i.e., focused on language in use) approaches to the study of sexism. Second, we present the results of a discursive analysis of "new" sexist discourse in the context of couple therapy. The study provides preliminary evidence that, despite endorsing egalitarian norms, couples studied continue to rely on gender binaries and remain entrenched in old-fashioned patterns of gender inequality. Implications of these results for the practice of couple therapy and for future research are discussed.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".