The Fading Goal of Gender Equality: Three Policy Directions that Underpin the Resilience of Gendered Socio-economic Inequalities
Bibliographic record
Abstract
Despite rhetorical commitment to gender equality as a fundamental value at international, supranational and national level, we continue to see economic and social inequalities in long-familiar areas. This article explores public policy communities' contribution to this resilience of inequalities in income, work and care by focussing on three responses to socio-economic restructuring and new social risks: labour force activation; the social investment perspective; treating gender as one of multiple discriminations. The article stresses the importance of making an analytic distinction between a policy discourse that displays “gender awareness” and one that identifies gender equality as a policy goal. By making this analytic distinction it is possible to identify a major change that has occurred in the last two decades in the universe of political discourse. This is the displacement of the gender equality discourse, despite rising gender awareness. The discourse on equality in income, work and care has been down-played within the universe of political discourse as other diagnostics either write gender equality out, rename women as “mothers,” or fold gender inequalities into a discursive frame of multiple and intersecting inequalities.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.092 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.009 | 0.013 |
| 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".