Reviews
Bibliographic record
Abstract
Abstract Planning Unplanned: Can Art have a Function? Towards a New Function of Art in Society, Barbara Holub and Christine Hohenbüchler (eds) (2015) Vienna: Verlag für Moderne Kunst, 256 pp., ISBN: 9783869840635, p/bk, £30.87 Industry and Intelligence: Contemporary Art Since 1820, Liam Gillick (2016) New York: Columbia University Press, 208 pp., ISBN: 9780231170208, h/bk, £26.00 Production Show 2016–2018: Prototyping/Discovering/Analysing, curated by Ruth Claxton and Gavin Wade, Eastside Projects, Digbeth, Birmingham, 1 October– 10 December 2016 The Birmingham Big Art Project (BBAP), shortlist exhibition, various venues in Birmingham, 2016–17 ‘The Hop Project’ by General Public (artists Elizabeth Rowe and Chris Poolman), various venues across Herefordshire, Worcestershire, Birmingham and the Black Country, 2016–17 Human Right, Stephen Willats, Middlesbrough Institute of Modern Art, Middlesbrough, UK, 4 March–4 June 2017 The Present in Drag, 9th Berlin Biennale for Contemporary Art, Berlin, 4 June–18 September 2016 Marie Yates, Works 1971–1979, Richard Saltoun Gallery, Great Titchfield Street, London, 24 June–22 July 2016
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.488 | 0.371 |
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".