MétaCan
Menu
Back to cohort
Record W3175346340 · doi:10.15626/hn.20214604

The #MeeToo Movement as an e-Discourse: Social and Legal Effects

2021· article· en· W3175346340 on OpenAlexaff
Marcel Danesi, Laura Ervo, Lukas Kindberg, Kerstin Nordlöf

Bibliographic record

VenueHumaNetten · 2021
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSexual misconductSocial mediaLegal consciousnessPersonality psychologyCriminologySocial psychologySocial movementPoliticsSexual assaultConsciousnessSociologyPsychologyGender studiesMedia studiesPolitical scienceLawPoison controlHuman factors and ergonomicsPersonality

Abstract

fetched live from OpenAlex

In this article, the #MeToo movement, as a socio-political form of e-discourse (discourse enacted on social media platforms and other types of online channels), will be examined in terms of the effectiveness of its discursive forms and the kinds of effects these have had on social consciousness generally with regard to sexual misconduct in the workplace, and in terms of the cases it has made famous against individuals via “trial-by-social-media,” and their outcomes in people’s lives. The specific cases discussed in this paper are those concerning well-known Swedish and American media personalities, which are assessed within a broad discourse and legal framework. Overall, we conclude that, while movement has had a profound effect on social consciousness, so far it has not impugned the validity of legal systems in countries such as Sweden and the United States.
 Keywords:#MeToo, e-discourse, social media trials, legal systems, sexual misconduct, sexual assault, sexual abuse

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.265
Teacher spread0.255 · 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 designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHumaNettenSame topicHate Speech and Cyberbullying DetectionFrench-language works237,207