Tweaking Harassment Through Tweets: A Critical Discourse Study of #MeToo
Bibliographic record
Abstract
The study aims to conduct an analysis of the discourse on #MeToo which the study takes to be the unmediated voice of the victims of sexual harassment to determine the ideology imbibed in the discourse. Teun A. van Dijk’s Ideology and discourse: A multidisciplinary introduction (2000) serves as the theoretical basis for the study as it attempts to categorize what constitutes harassment for the victims by looking at 3000 tweets posted over thirty days. The critical analysis of the data reveals that one of the most lacking elements in the life of the victims of sexual harassment is an acknowledgment of harassment as harassment. Most of the victims are abused, shamed and silenced when they try to share their panic experiences of life. The study shows that sexual harassment is a widespread social epidemic and can occur to a person of any age group, at any place and in any type of relationship. The study is significant as it presents the unmediated view of the victims of sexual harassment and compares it with the existing view of harassment. The significance of the study also lies in the fact that it analyzes the features of Twitter to determine their role in shaping the discourse. Previous studies on harassment talk about the issue or explore its features and types through various angles, but none of them focuses on the voice of the victims themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".