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Record W2738173394 · doi:10.3167/ghs.2017.100201

Technological Nonviolence and Girls: Creating a Counter Discourse

2017· article· en· W2738173394 on OpenAlexfundno aff
Claudia Mitchell

Bibliographic record

VenueGirlhood Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsSociologyPsychology

Abstract

fetched live from OpenAlex

The idea of devoting a special issue of Girlhood Studies to what Jonathan Bock (2012) calls technologies of nonviolence comes at a critical time in girlhood studies. On the one hand, technology-especially digital technology-and various social media platforms are firmly entrenched in the everyday lives of many young people around the world. On the other, questions regarding who has access to technology and how technology is used and abused continue to dominate the fields of girlhood studies in particular and of youth studies more broadly. Nowhere is this more evident than in relation to violence and social media in the lives of girls and young women. The many movements and organizations-some of them, like Take Back the Tech, 1 global in scope-are playing a key role in contributing to altering the landscape.

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)0.011
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0230.076
Scholarly communication0.0220.019
Open science0.0020.020
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0070.001

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 head0.100
GPT teacher head0.320
Teacher spread0.221 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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