Bibliographic record
Abstract
There have been many recent media reports about the online harassment of women journalists working in technology, particularly the video gaming industry. However, little research has focused on this aspect, by looking at specific occupations, or analysing the implications for women and society. This paper is a feminist study of the experiences of sexist abuse of a sample of women journalists writing about technology. It is a commentary on the results of a questionnaire-based study of 102 women (and their approximately 300 comments) that work in what has emerged as one of the frontlines of the struggle for gender equality. The research looks at the extent of the abuse, the harm it causes and how women are reacting to it. Most of the participants have experienced abuse, many have changed their working practices and some have disguised their identity to avoid it. An examination of their comments suggests that sexist abuse is now often normalised, alongside a new kind of “invisible” feminism. It also reveals a mood of defiance and an appetite for radical change to address the problems of exclusion and loss of identity. Overall, results indicate that the abuse is damaging women’s lives and impacting journalism and society in a negative way.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".