Bibliographic record
Abstract
A collaborative zine made for World Book Night, which resulted in a free download DIY publication www.bookarts.uwe.ac.uk/13smallgt.htmThis year the work was a tribute to Raymond Carver's collection of stories Cathedral (1983), some of which were used as the basis for Robert Altman's film Short Cuts (1993). In particular we chose the story A Small, Good Thing as the starting point for this year's collaboration. An open call this website invited people to do a small, good thing, write it out and send it to us.All contributions were received by midnight on 22nd April. On World Book Night we made a low-fi, video in a very homemade style of the original film clip for Bob Dylan's Subterranean Homesick Blues, using those small, good things. contributorsAnonymous (quite a few), Craig Atkinson, Annabel Barton, John Bently, Sarah Bodman, Phil Bowden, Angie Butler, Simon Butler, Arthur Buxton, Nancy Campbell, Anna Corbett, Lilla Duignan, Su Fahy, Hazel Grainger, Charlotte Hall, Paul Laidler, Teri Makassih, Imi Maufe, Natalie McGrorty, Allil Nangi-Ud, Anne On, Jeff Rathermel, Gwen Simpson, Simon Smith, Tom Sowden, Tracy Stanton, David Sully, Gail Sully, Tribe, Barrie Tullett, Elizabeth Willow, Mark Wingrave, Philippa Wood. Special thanks to Simon Butler and Simon Smith for the original soundtrack.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 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".