Bibliographic record
Abstract
This output (The Blue Notebook Vol 15 #2 ) builds on Mosely’s research output “abbe 2020”. It continues Mosely’s contribution to the field in addressing the call from scholars in the field to advance the critical nature of the field (artists books) with a developing critical terminology and corresponding discourse. \n\nLeading up to abbe 2020 Mosely secured an agreement with Dr Sarah Bodman (the founding editor of The Blue Notebook) to submit academic papers from abbe for publication in The Blue Notebook. The Blue Notebook has emerged as the leading journal within in the field of artists books and holds a broad base of subscribers and readers across the globe. To realize this opportunity Mosely initiated a blind peer review collective drawn from the academic community across Australia. Following abbe’s participation in the 2020 Arlis/ANZ Bicentennial Conference, he selected papers delivered at abbe 2020 and co-ordinated their peer reviewing. Papers accepted through peer review were then professionally edited with financial support from QCA and submitted to the Blue Notebook for blind refereeing.\n\nThis volume of the journal extends the international representation of four Australian and one UK artists / writers contributing to the field of artists books (and of print culture). Five articles were published in Volume 15 #2.\n\nMarian Crawford, \nPaul Uhlmann,\nCaren Florance,\nAna Paula Estrada,\nAngie Butler
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.468 | 0.365 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".