Bibliographic record
Abstract
The United States of America went through a radical self-examination in 2020. The shock of the Covid pandemic combined with economic and social dislocations, followed by the police murder of George Floyd, forced a reckoning on race, racism, and state violence. That same year, Opal Tometi, a co-founder of Black Lives Matter, wrote that she believes “that what we are witnessing now is the opening up of imaginations, where people are beginning to think more expansively about what the solutions could be”.[mfn]Isaac Chotiner, “A Black Lives Matter Co-Founder Explains Why This Time Is Different,” The New Yorker, June 3, 2020. See article[/mfn] In the Design Media Arts department at UCLA, I teach a course titled Design Futures, that has historically been an incubator for hybrid research and production. In response to the issues of the moment, we look to investigate how design theory and praxis could open up a series of new, more inclusive futures. The question before us was: what can design do to help us through this moment? How could we conceive or design futures that we actually want to live in, rather than as briefs from commercial interests? We determined one answer to a series of design research provocations, that we termed Solve for (x)Futurisms. These ran the gamut from Afro Futurisms to Latinx Futurisms, Indigenous Futurisms to Queer Futurisms. The group researched extant practices, modeled design ethnographies, and then mocked up their own bespoke future scenarios. What follows is a visual primer of the (x) Futurisms of a small group of young makers on the edge of the Pacific Ocean in a year that was definitively in and of the 21st century. Peter Lunenfeld
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 | 0.053 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.076 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".