#MeToo and #ShoutYourAbortion: Claiming Standing and Exploding the Private Sphere
Bibliographic record
Abstract
Abstract This paper analyses the recent viral #MeToo and #ShoutYourAbortion campaigns and argues that examining them illuminates our thinking about privacy and standing.The paper argues that one of the aims of these campaigns was to debunk the view that women did not have standing with respect to matters concerning sexual harassment and reproductive care. The myths the campaigns sought to discredit – myths about sexual harassment and assault and abortion – involve victim-blaming, and one thing we do when we victim-blame is deny that the victim had standing. This paper also argues that women proved they had standing through these campaigns by revealing what was private. This is, I argue, a way of ‘exploding’ the private sphere as MacKinnon famously put it. By looking to these campaigns, we can see that their strategy relied on the value of privacy.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".