Why tech/Why not? A report on the 2003 Subtle Technologies Conference
Bibliographic record
Abstract
I recently attended the 6th annual Subtle Technologies conference (www.subtle technologies.com), held in Toronto at Innis College (May 22 to 25, 2003). This is a conference that is situated at the blurred boundaries between science and art. Every year this conference, organized by Jim Ruxton with the participation of InterAccess Electronic Media Arts Centre, gathers an eclectic mix of scientists and artists working with technology to present their work. The areas of specialization include robotics, quantum physics, artificial intelligence, genetics, mathematics, biology, space and time, musical organisms, dance, sensors, interfaces, architecture, and art. This year’s theme, “Ground,” focused loosely on new technologies and architectural practice; there were presentations about locative mapping and positioning devices, industrial culture, biotechnological architecture, surveillance, the brain basis of musical performance, interacting galaxies or gravity as art, robotic art projects, and the ethical nature of scientific communities.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".