Doxxing, Privacy and Gendered Harassment. The Shock and Normalization of Veillance Cultures
Bibliographic record
Abstract
We conducted 15 in-depth interviews with women and men in Germany, Switzerland, Finland, Canada, and the United States who were victims of doxxing. The goal was to understand their experiences, their responses, and the consequences they faced. We understand doxxing as a complex, gendered communicative process of harassment. Doxxers use digital media technologies to expose personal information without consent given by those to whom the personal information belongs. We apply a feminist approach to surveillance studies to doxxing, focusing on the constructions of daily, habitual, and ubiquitous assemblages of veillances that disproportionately impact vulnerable individuals. We found that gendered aspects shaped the flow and suspected intent of doxxing and subsequent harassment. Victims experienced uncertainty, loss of control, and fear, while law enforcement and social media providers only helped in a few cases to pursue doxxers or remove unwanted personal information. We ultimately extend the definition of doxxing by considering the ubiquitous nature of information shared online in gendered veillance cultures. Our findings lead us to advocate for protecting the contextual integrity of entering personal information into expected, intentional, or desired spaces.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".