Insurgent Design Coalitions: The history of the Design & Oppression network
Bibliographic record
Abstract
Design research is gettng interested in social movements in recent years. Organizing tactics like coaliSon-building have been taken from civil rights movements and turned into operaSve concepts such as designing coaliSons that point towards converging interests. As such, this concept cannot support social movements, which are not formed by common interests, but by pressing social needs ignored in official and everyday poliScs. This advances further the revision of the designing coaliSon concept based on feminist literature and on the authors' experience in weaving the Design & Oppression Network in Brazil. This network was formed in 2020 by design professors, students, and professionals from all over Brazil, as well as from other countries. From its incepSon, the network was concerned with the LaSn-American reality — colonized, culturally invaded, underdeveloped, and oppressed in various ways by the Global North. The network approaches design as a pedagogical and criScal process so that the producSon of design space becomes an opportunity for listening, reflecSon, dispute, synthesis, mutual care, and insurgence acSons against all forms of oppression. From this experience, we propose the alternaSve concept of insurgent design coaliSons to deepen design engagements with social movements.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".