Dyscorpia: Exhibition, Enterprise Square Galleries, Edmonton, Canada
Bibliographic record
Abstract
The works presented as part of this exhibition are part of the ongoing research 'Genes, Memes & Time Machines' and are derived from the research project undertaken as part of the EpiGeneSys European Network of Excellence (Associate Artist with this EU Funded FP7 Framework project – 2011 - 2016) - including the output: #HASHTAG: Visions of Epigenetics/Visions d’Epigenetique (ARR 2016/2017) and (re)visions (ARR /2017). Type of Output: Exhibitions/events 2019 Exhibition Title: DYSCORPIA Works included: EpiGeneScapes 1- 4 Drawn for Thought (Folio of 12 Prints with Title Page) ENTERPRISE SQUARE GALLERIES, 10230 Jasper Ave. Edmonton, Canada. 23rd April – 30th June 2019 Symposium: 27th April 2019 Dyscorpia is an exhibition gathering artists and thinkers in visual art, design, contemporary dance, medical humanities, virtual reality, sound creation, computer science, and creative writing in order to question what it means not to know the limits of our bodies. Dyscorpia is not science-fiction. Dyscorpia is historical time and biological time entangled. It forces past and future in a deadlock, so the present can be squeezed inside out for you to see. Curated by Marilene Olivier Other artists include: Isabelle Van Grimde, Liz Ingram and Sean Caulfield
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.694 | 0.326 |
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".