Bibliographic record
Abstract
The Artists' Book Yearbook (ABYB) serves as a resource for artists, lecturers, students, collectors and researchers. The bi-annual publication includes essays and information on many aspects of the book arts, artists' listings, information on book arts galleries, archives and collections, book arts courses, events, journals, bibliographies and reference publications, studios and websites, with book arts contributors from around the world. \n \nContributions by: Tanya Peixoto UK, John Bently UK, Doro Boehme-School of Art Chicago, USA, Guy Begbie UK, Joanna Hoffmann Poland, Carinna Parraman UK, Astrid van Baalen, The Netherlands, Patsy Payne Canberra School of Art, Australia, Deirdre Lawrence Brooklyn Museum, USA, Gandha Key Zybooks UK, Edward Summerton UK, Elizabeth Hobbs UK, Mike Brunwin London College of Printing, UK, David Shrigley UK, David Kirby UK, Sue Cunliffe UK…and many more.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.106 |
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".