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Record W7115931018 · doi:10.5206/fpq/2025.4.18657

Early Modern Feminists on Wooing as a Gendered Epistemic Harm

2025· article· en· W7115931018 on OpenAlexaffvenue

Bibliographic record

VenueFeminist Philosophy Quarterly · 2025
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsMcMaster University
FundersUniversität Paderborn
KeywordsAgency (philosophy)HarmCoercion (linguistics)Power (physics)Relation (database)Autonomy

Abstract

fetched live from OpenAlex

In this article I explore how three early modern European feminists—Marie de Gournay, Margaret Cavendish, and Mary Astell—discuss wooing in surprisingly similar ways. Independently and without the benefit of a personal or intellectual relationship, all three highlight how wooing is characterized by deception, insincere flattery, and occasional coercion to secure consent to marriage; they problematize how wooing tricks women into consenting to their own subordination. This is a feminist social epistemological project: Gournay, Cavendish, and Astell recognize wooing as a gendered socio-epistemic harm, for wooing undermines women’s abilities to exercise their epistemic agency over making what was often the single most consequential choice in their lives—who to accept as husband. I argue that for Gournay, Cavendish, and Astell, wooing not only is a gendered socio-epistemic harm but also manifests an epistemic injustice, hermeneutical obscuring, which was deployed by wooers in service of maintaining patriarchal power hierarchies. For Gournay, Cavendish, and Astell, the social practice of wooing manifests patriarchal control via a kind of gendered epistemic tyranny. Successful wooing frustrates epistemic capacities, entrenches gender hierarchies, and traps women in the epistemically and politically subordinating relation of marriage.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.511
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.006

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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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