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Record W4282575908 · doi:10.1177/14614448221099187

Viral paradox: The intersection of “me too” and #MeToo

2022· article· en· W4282575908 on OpenAlexaff
Alicia Boyd, Bree McEwan

Bibliographic record

VenueNew Media & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntersectionalitySociologyQueerScholarshipOppressionInvisibilityHarassmentHuman sexualityGender studiesAffordanceSocial psychologyPoliticsLawPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Scholarship on #MeToo has examined the feminist underpinnings of the movement and affordances of digital platforms to create space for telling stories of sexual harassment and violence. This essay makes a different contribution, in that we seek to understand the impact of the viral version of #MeToo on the established primarily Black community developed by Tarana Burke. In this essay, we use the framework of intersectionality and organizational paradox to examine the differences in the social construction of the two versions of the movement. The framework of intersectionality allows us to examine how the viral version of #MeToo perpetuated by Alyssa Milano reified the social construction of inequalities and interlocking systems of oppression for Black and other women of color. The article examines the effects of Milano’s entrance into the “me too” space on the community built through Burke’s “me too” movement. We identify an illumination/occlusion paradox that creates the illusion of inclusivity, creates difficulty in community boundary management, and allows for outsider gaze into a previously safe space. We argue for moving beyond the considerations of assigning credit for the movement and instead consider the impacts of the paradox of the original community experiencing erasure through the abrupt and swift increased visibility of the hashtag.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.101
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.032
GPT teacher head0.284
Teacher spread0.251 · 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.

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

Citations25
Published2022
Admission routes1
Has abstractyes

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