A Tale of Two Hashtags: More Than Absolute Opposition in an Affinity Space Formed by Sexual Harassment
Bibliographic record
Abstract
Digital activism on social media has a strong relationship with hashtags and this activism on social media tends to produce multiple protest and counter-protest hashtags. This case study explores the discursive constructions across competing hashtags (#KickVic and #IStandWithVic) that represent the protest and counter-protest surrounding the sexual harassment allegations of Vic Mignogna. Through the synergized approaches of corpus linguistics and critical discourse studies, I investigate the perpetuation of patriarchal ideologies within this affinity space. I constructed three tweet-based corpora for analysis: #KickVic, #IStandWithVic, and mixed. Keywords, semantic themes, and discursive constructions of women demonstrate that these hashtags subvert and perpetuate a patriarchal social order. While the perpetuation of these ideologies is more present within the #IStandWithVic corpus and the mixed corpus, tweeters also use #KickVic to perpetuate them. Discussion and contributions to affinity spaces and discursive constructions of these digital activism hashtags are included in this investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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 teacher head, 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".