Integrating gender in anti-cybercrime capacity-building: a toolkit
Bibliographic record
Abstract
Cybercrime has multiple gendered impacts. Ransomware attacks on healthcare systems can expose data and information that render women, LGBTIQ people and other minoritized groups vulnerable because of societal discrimination. Disruptions to online systems for public services can impede access to vital services – including sexual and reproductive health services – for people who already face barriers to access. The hacking of social media accounts and unlawful accessing of personal information is a risk for everyone, but the consequences of unauthorized sharing of intimate content – including images that have been altered or artificially generated – are often most serious for women and marginalized groups. Cybercrime defences must therefore ensure appropriate and proportionate protection for all vulnerable groups. This toolkit has been designed for practitioners working to integrate gender considerations in anti-cybercrime capacity-building activities. Using a set of example projects, it offers clear steps to promote the gender-sensitive design and implementation of a wide range of capacity-building activities. Presented as both an interactive digital resource and a PDF download, the toolkit is intended to enable everyone responsible for developing and strengthening the skills, abilities and resources of organizations and communities to survive and thrive in cyberspace – and those who support them in this work – to do so with full regard for gender equity and sensitivity.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".