'What Have You Done for Me Lately?' Reflections on Redeeming Privacy for Battered Women
Bibliographic record
Abstract
Based largely on privacy's ongoing use to justify nonintervention in situations of domestic violence, many feminists have rejected privacy as antithetical to women's equality. This chapter revisits the feminist rejection of privacy in the context of domestic violence by re-conceptualizing woman abuse as implicating women's privacy interests: physical, sexual and emotional abuse clearly infringe a woman's right to be let alone as against her battering partner, and violate her right to create boundaries between herself and others. State nonintervention and police reluctance to enforce protection orders in the name of privacy must then be seen as privileging a man's right to be let alone from state interference over a woman's right to be let alone from a battering partner. Understood as a situation of competing privacy interests, a battered woman may require quantitatively more privacy as against an abusive partner and qualitatively more effective means to create and enforce those privacy boundaries to ensure her physical and emotional safety. The paper concludes that privacy should be reinvigorated as a tool in the fight against domestic 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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".