Gendering the Comparative Analysis of Welfare States: An Unfinished Agenda», Colloque RC19 Social Policies: Local Experiments, Traveling Ideas, Université de Montréal, août, consulté le 7 mars 2010
Bibliographic record
Abstract
Can feminists count on welfare states—or at least some aspects of these com-plex systems—as resources in the struggle for gender equality? Gender analysts of “welfare states ” investigate this question and the broader set of issues around the mutually constitutive relationship between systems of social provision and regula-tion and gender. Feminist scholars have moved to bring the contingent practice of politics back into grounded fields of action and social change and away from the reification and abstractions that had come to dominate models of politics focused on “big ” structures and systems, including those focused on “welfare states. ” Concep-tual innovations and reconceptualizations of foundational terms have been especially prominent in the comparative scholarship on welfare states, starting with gender, and including care, autonomy, citizenship, (in)dependence, political agency, and equality. In contrast to other subfields of political science and sociology, gendered insights have to some extent been incorporated into mainstream comparative scholarship on welfare states. The arguments between feminists and mainstream scholars over the course of the last two decades have been productive, powering the development of
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".