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Record W4402931254 · doi:10.1016/j.wsif.2024.102996

A lack of understanding: Unpacking the transformative power of women's anger in politics

2024· article· en· W4402931254 on OpenAlexaff
Hailey Murphy

Bibliographic record

VenueWomen s Studies International Forum · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsUnpackingTransformative learningAngerPoliticsPower (physics)Gender studiesSociologyPsychologySocial psychologyPolitical scienceDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

This paper argues that women's rage is a legitimate and transformative response to systemic injustices and gender-based oppression. By examining the philosophical and feminist traditions the paper demonstrates how rage differs from anger by driving individuals towards action. Traditional political philosophy has often emphasized rationality and self-control, neglecting the significance of emotions and the contributions of women. The analysis reveals that women's anger, particularly when viewed through intersectional lenses, is frequently dismissed, further entrenching marginalization. By recognizing and embracing women's rage, society can address the root causes of injustice and empower women to challenge oppressive structures. This paper highlights the importance of understanding and validating women's emotional responses as crucial for achieving social and political change. • Examines women's rage as a change catalyst • Historical and theoretical insights on gender activism • Links rage to feminist political transformation • Discusses modern implications of women's rage 5. Suggests future research in feminist discourse

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.151
GPT teacher head0.445
Teacher spread0.294 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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