Pathways to Gender Equality in Design
Bibliographic record
Abstract
This chapter reflects on some of the challenges surrounding the broader context of design. Strengthening intersectionality in systems design requires data not just concerning women but also gender minorities, that can be shared and analyzed; it requires ‘finding’ those who should be part of research and creating safe spaces for them. A feminist perspective encourages to prioritize the ‘personal’, recognizing it as a political act of resistance. At the heart of gender equality is the collective dimension of women’s citizenship and their social capital. Alliances in support of gender/social justice in design need to be built using strategies such as participatory infrastructuring and ‘institutioning’ but also acknowledging the importance of feminist trade unionism. The final points raised in this chapter are: how to connect with moves to decolonize discourses and practices of IT design; how to take a feminist perspective with regard to teaching; how to get funded and published; and how to challenge the business models of the software industry that undermine technical flexibility and make gender-sensitive design approaches difficult to implement on a larger scale.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".