Four Ways to Feminist Research Praxis: Lessons from Practice in AI Ethics and Policy Research
Bibliographic record
Abstract
Background: The last five years have been dizzying for anyone concerned with AI policymaking and regulation. This briefing describes four ways to enact feminist research praxis in technology policy research. Analysis: Drawing on lessons from the JUST AI Network in the United Kingdom and insights from both feminist and disability studies, this study critiques and redevelops relationships between discourse, knowledge, and institutional structures. Conclusions and Implications: To transform technology policy work, we must prioritize greater reflexivity, adopt networking approaches that channel resources to less-connected actors, and establish and sustain radical groupings and relationships to produce accessible futures, even when these efforts do not yield short-term benefits.
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 | 0.316 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.031 | 0.272 |
| Scholarly communication | 0.050 | 0.064 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.023 | 0.039 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".