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Record W4404718402 · doi:10.4324/9781003539971-17

Introduction

2024· book-chapter· en· W4404718402 on OpenAlexvenueno aff
Barbara Caine

Bibliographic record

VenueCrossing boundaries · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

For the last ten years, feminist historians have been engaged in extensive debate about the history of feminism. The very limited interest in earlier feminist movements which was evident among those interested in women’s studies in the early and mid-1970s (see e.g. Lerner, 1969), has given way to an intense and constantly increasing interest in every aspect of the theory and the politics of all known feminists and all known women’s movements. Scholars working in the field have greatly expanded the range of questions and issues which are encompassed in discussion of the history of feminism, so that it has now become central to the study of women’s history. Until the late 1970s, those historians engaged in the study of feminist movements concentrated on the various campaigns which were mounted in the nineteenth and early twentieth centuries to achieve political and legal rights for women. Feminism within this framework was actually defined simply as ‘the deliberate attempt to achieve equality between the sexes in the political, economic and domestic spheres’ (Banks and Banks, 1965). While this framework continues to exist (e.g. Evans, 1979), it is increasingly coming to occupy a marginal position within the field, while the question of feminist theory and feminist analyses, of women’s support networks and independent activities have been given much greater prominence. As a result, the scope of discussions of feminism within an historical framework has become much broader, while its subject matter has been greatly enriched. Feminist political goals and strategies have been recognised as simply one aspect of a very much more complex phenomenon and are now studied in relation to broader questions about the ways in which women have organised their lives, analysed their experiences, and coped with the many situations and problems which they face.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0200.023
Scholarly communication0.0190.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.035
GPT teacher head0.307
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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