MétaCan
Menu
Back to cohort
Record W2023801407 · doi:10.1177/0044118x05283482

“No Boundaries”? Girls’ Interactive, Online Learning About Femininities

2006· article· en· W2023801407 on OpenAlexaffabout
Deirdre M. Kelly, Shauna Pomerantz, Dawn H. Currie

Bibliographic record

VenueYouth & Society · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFemininityAgency (philosophy)Transformative learningSubjectivityEmpowermentPower (physics)SociologyHarassmentCyberspaceGender studiesRomanceHuman sexualityMasculinityQualitative researchPsychologySocial psychologyThe InternetPedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article explores girls’ learning about issues of femininity that takes place in the presence of others online, connected through chat rooms, instant messaging, and role-playing games. Informed by critical and poststructuralist feminist theorizing of gendered subjectivity, agency, and power, the article draws from qualitative interviews with 16 girls in Vancouver, Canada. Girls reported that online activities allowed them to rehearse different ways of being before trying them out offline, where they might have been reined in for going against perceived expectations for their gender. The article shows that girls enjoyed playing with gender and being gender rebellious. They practiced taking more initiative in heterosexual relationships than is currently authorized in prevailing rules of romance. Without necessarily challenging the underlying gendered power inequalities, some battled back in cyberspace against sexual harassment. In the conclusion, the authors reflect on the implications for girls’ individual and collective empowerment and for transformative pedagogy.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.011
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.029
GPT teacher head0.295
Teacher spread0.266 · 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 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

Citations68
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueYouth & SocietySame topicGender, Feminism, and MediaFrench-language works237,207