Bibliographic record
Abstract
Cripping Cyberspace: A Contemporary Virtual Art Exhibition would not have been possible without the incredible commitment and support of Jay T. Dolmage, Editor of the Canadian Journal of Disability Studies, and Geoffrey Shea and Libby Shea from the Common Pulse Intersecting Abilities Art Festival and Symposium. I thank them tremendously for inviting me to curate this project, through which my skills and ideas around curating have most certainly been challenged. I am also grateful to Jay for collating all the exhibition materials and formatting the special issue of the Canadian Journal of Disability Studies in order to showcase Cripping Cyberspace, and providing the artists with technical advice. Libby and Geoffrey have been efficient administrators of the artist and curator contracts, travel arrangements and finding the appropriate resources. It has been an honor to work with artists Katherine Araniello, Cassandra Hartblay, Sara Hendren and m.i.a. collective (Arseli Dokumaci, Antonia Hernández, Laurence Parent and Kim Sawchuk). Thank you for being willing guinea pigs in this wonderful experiment in cyberspace, and participating in the additional Skype artist interviews and audio description process. Finally, I am grateful to Alexandra Haagaard for providing the excellent written transcripts of the Skype artist interviews.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.027 |
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".