I get by with a little help from my friends: The ecological model and support for women scholars experiencing online harassment
Bibliographic record
Abstract
This article contributes to understanding the phenomenon of online abuse and harassment toward women scholars. We draw on data collected from 14 interviews with women scholars from the United States, Canada, and the United Kingdom, and report on the types of supports they sought during and after their experience with online abuse and harassment. We found that women scholars rely on three levels of support: the first level includes personal and social support (such as encouragement from friends and family and outsourcing comment reading to others); the second includes organizational (such as university or institutional policy), technological (such as reporting tools on Twitter or Facebook), and sectoral (such as law enforcement) support; and, the third includes larger cultural and social attitudes and discourses (such as attitudes around gendered harassment and perceptions of the online/offline divide). While participants relied on social and personal support most frequently, they commonly reported relying on multiple supports across all three levels. We use an ecological model as our framework to demonstrate how different types of support are interconnected, and recommend that support for targets of online abuse must integrate aspects of all three levels.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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 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".