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Record W3185661760 · doi:10.1177/20436106211027341

Girls and activism in a neoliberal time: How teen girls from Toronto negotiate care, activism, and extraordinary girlhood

2021· article· en· W3185661760 on OpenAlexaffabout
Tina Belinda Benigno

Bibliographic record

VenueGlobal Studies of Childhood · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsYork University
Fundersnot available
KeywordsNegotiationPower (physics)SociologyGender studiesSocial activismFeminismGirlSocial movementMedia studiesPoliticsPolitical sciencePsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Recently, a number of prominent teen girl activist leaders have been gaining the world’s attention, but how do girls not in the public eye and with less social power think about activism? Moreover, how do girls who may not exclusively define themselves as activists, negotiate their own desire to contribute to social change with challenges they identify as holding them back from doing so? Through qualitative research with eight teenage girls in Toronto, I explore the ways these teen girls define the “activist,” their role in activism, and the challenges holding them back from being more active. My methodology is congruent, reflecting my feminist and youth studies commitment to girls leading research, and my findings indicate that such an approach is crucial in order to truly understand how girls with less social power and public visibility experience the world and their roles within it. Doing so also challenge pre-conceived notions and standards of extraordinary girlhood. The findings coincide with what Catherine Rottenberg refers to as neoliberal feminism. The extraordinariness implicit in visible activism framed the girls from my study’s views on what it would take to be a true activist themselves, which was both intimidating and also at times is in contention with their monumental care and concern for loved ones.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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 designObservational
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

Citations3
Published2021
Admission routes2
Has abstractyes

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