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Record W3159097015 · doi:10.1145/3449185

Parsing the 'Me' in #MeToo

2021· article· en· W3159097015 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsHarassmentCasteGender studiesQueerPatriarchySocial mediaSociologyPopulationPolitical scienceLaw

Abstract

fetched live from OpenAlex

India's #MeToo movement began in late-2018, and was largely a platform for some privileged women sharing their accounts of sexual harassment. Beyond issues of access to digital technology, our paper investigates why various sections of India's female and LGBTQ+ population chose not to engage with the #MeToo movement. Focusing on experiences with sexual harassment, we conducted 44 qualitative interviews with middle-class working women, feminist and queer activists, academics, and other stakeholders working against gender-based violence, to understand their perspectives on #MeToo. Our paper explores why some survivors bypass the legal infrastructure to speak out against sexual harassment using #MeToo, while others choose not to participate despite having access to social media platforms. Using the lens of infrastructure, we outline the imbrication of social media movements with existing social norms and legal infrastructures. Further, we highlight how infrastructural politics are connected to patriarchy, colonialism, caste, class, and gender struggles.

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.

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)0.001
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 score0.341
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.096
GPT teacher head0.375
Teacher spread0.279 · 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