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Record W4362722592 · doi:10.1111/jftr.12499

Critically feminizing family science: Using femme theory to generate novel approaches for the study of families and relationships

2023· article· en· W4362722592 on OpenAlexaff
Rhea Ashley Hoskin, Toni Serafini

Bibliographic record

VenueJournal of Family Theory & Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of WaterlooSt. Jerome's University
Fundersnot available
KeywordsFemininityRelational theoryPsychologyHuman sexualityGender studiesPromotion (chess)Feminist theoryFriendshipField (mathematics)Social psychologyDevelopmental psychologySociologyFeminismPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Abstract How do values, beliefs, and assumptions about femininity shape relational experiences? To answer this question, we critically feminize family science by applying femme theory to the field. Through this analysis, we present some of the ways that femmephobia (i.e., the systematic devaluation and regulation of femininity across all bodies and identities) is established in childhood and perpetuated throughout various relational contexts across the life course. Specifically, we examine how femmephobia is socialized via families, systematically normalized and perpetuated throughout childhood, and how it shapes gender‐based violence risk, perpetration, and tactics. We demonstrate how femme theory illuminates the importance of disentangling the intersectional axes of gender, sexuality, and gender expression, thereby generating novel approaches for family and relationship science and interventions that promote systemic social change. Subordinating and regulating femininity affects all individuals, making its disruption of critical importance for the prevention of gender‐based violence and the promotion of healthy families and relationships.

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)0.024
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.413
GPT teacher head0.416
Teacher spread0.004 · 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.

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

Citations20
Published2023
Admission routes1
Has abstractyes

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