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Record W4291000374 · doi:10.1215/00182702-10085587

Women and Economics: New Historical Perspectives

2022· article· en· W4291000374 on OpenAlexaff
Cléo Chassonnery-Zaïgouche, Evelyn L. Forget, John Singleton

Bibliographic record

VenueHistory of Political Economy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHistoriographyMainstream economicsNarrativeHistory of economic thoughtMainstreamPositive economicsSchools of economic thoughtHuman development theorySociologyField (mathematics)Heterodox economicsPoliticsSocial scienceSection (typography)Applied economicsEconomicsPolitical scienceNeoclassical economicsLaw

Abstract

fetched live from OpenAlex

Abstract This essay is the introduction to the 2022 supplemental issue of History of Political Economy, titled Women and Economics: New Historical Perspectives. We first reflect on the historiography of economics and the relative absence of women and gender in the mainstream of the field. Three approaches to the history of women and economics are delineated: making visible women economists, outlining the impact of including women in a broader narrative about economics, and analyzing gender metaphors in economic thought. We then preview and describe the nine contributions included in the volume. In the last section, we consider what is next for this research agenda, arguing that there are two important challenges to historians of economics. The first challenge concerns the consequences of delegating women and gender to a separate history. The second challenge concerns the “silences” of unwritten, undeveloped, and unpreserved work in the history of economics and how the community engaged with the past of economics should reflect on these.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0060.033
Scholarly communication0.0090.010
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.041
GPT teacher head0.251
Teacher spread0.209 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations13
Published2022
Admission routes1
Has abstractyes

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