Welcome to the Department of Utopian Arts and Letters
Bibliographic record
Abstract
The Department of Utopian Arts and Letters (DUAL) is a project of public dreaming born out of our deepest desires for decolonization, climate justice, and collective liberation. The Department of US is a creative educational experiment to interrupt apathy and denial and disrupt naturalized habits of imagining the future through relational rigour, skill building, resource sharing, and planning for systemic change and interconnected social and technological innovation. Our faculty of diverse community-expert artists are agents of imagination and unlearning. The Department of US is informed by critical practices of deschooling and global citizenship education. Our courses are gift based and non-transactional. We know we will fail to live up to our lofty objectives, despite our best intentions. DUAL emerges from concerns about educational models that endeavour to support social change through a basic ‘description-prescription formula’ that begins with a description of the primary problem with our existing social, political, and economic systems and is followed by a prescription that purports to ‘solve’ that problem. Our classes, Plural Utopians of the Future (POUFs), are open to all students across all institutions. The department accepts problematization of the stability, certainty, and security of conventional course streams that repeat colonial patterns of knowing, in favour of self-guided un/learning and open-ended pedagogy at your own pace. The initial staff and faculty consists of Meghan Moe Beitiks, Aisha Lesley Bentham, Jenn Cole, Sanita Fejzić, Ian Garrett, Marilo Nuñez, and Kimberly Skye Richards.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.320 | 0.138 |
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".