Bibliographic record
Abstract
CC BY-NC-SA 4.0 May 2014 Shift/Work (Neil Mulholland, Dan Brown) Attribution-NonCommercial-ShareAlike 4.0 International Performed by Shift/Work at: Kochi-Muziris Biennale, India March 2017 | University of Agder, Kristiansand, Norway August 2017 | Listaháskóli Íslands Sept 2016 | Malmö Art Academy 2014 and 2016 | ESW 2014 Peer reviewed papers presented at Paradox: Alternative Zones: Uncovering the Official and the Unofficial in Fine Art Practice, Research and Education 2015 (The University of Arts Poznan, Poland), the 4th International Visual Methods Conference 2015 (Brighton University, England), International Teaching Artists Conference: Best, Next and Radical Practice in Participatory Arts (ITAC3) 2016 (University of Edinburgh, Scotland) and the International Society for the Scholarship of Teaching and Learning 2017 (University of Calgary and Mount Royal University, Canada). Shift/Workshops are composed and disseminated by being performed. Playing the score leads to it being re-calibrated for future performances. Composition is scaffolded with an academic and artistic community of ‘Shift/Workers’. Shift/Workers compose and play-test each other’s workshop scores before calibrating them through collective peer-review. Scores are then re-performed, iteratively, at international peer-reviewed artistic and scholarly events.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.807 | 0.629 |
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".