Exhibition Reviews
Bibliographic record
Abstract
Abstract Sam Durant, Proposal for White and Indian Dead Monument Transpositions, Washington, dc Los Angeles County Museum of Art, 3 August – 30 November 2014 Sona Safaei, V+1 8–11, Toronto, 16–30 August 2014 Terms of Engagement: Averns, Feldman-Kiss, Stimson Curated by Christine Conley, Agnes Etherington Art Gallery, Kingston, 26 April – 10 August 2014, also exhibited at Mount Saint Vincent University Museum, Halifax, 18 January – 9 March 2014, and the Esker Foundation, Calgary, 27 September – 14 December 2014 Camilla Singh, Uniforms for Non-Uniform Work Curated by Emelie Chhangur, Art Gallery of York University, Toronto, 2 April – 15 June 2014 Fiona Tan, Inventory Curated by Adelina Vlas, Philadelphia Museum of Art, 14 December 2013 – 11 May 2014; originating at MAXXI National Museum of XXI Century Arts, Rome, 27 March – 8 September 2013
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.703 | 0.464 |
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".