Embracing intersectional feminist thinking in design processes
Bibliographic record
Abstract
Intersectional feminist thinking has the potential to promote an understanding of the dynamics of oppression in design and foster ways to minimize them. The study presented in this article explores how it has been embraced in the design field so far. A literature review process and an analysis of three cases characterize the study presented in this article. The research shows that although emerging, research on embracing intersectional thinking in design is relatively scarce. The literature review enabled the identification of three design approaches that officially state the adoption of an intersectional lens. These approaches were analysed against two core principles of intersectionality – pluralism and contextualization. Their description and analysis are reported here to exemplify how intersectional thinking has been implemented into design processes and to highlight existing strengths, challenges and opportunities. The article offers some reflections on future directions for intersectional design approaches to engage more closely with intersectionality.
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 | 0.052 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".