Bibliographic record
Abstract
‘…Simon, you’re an artist, I’ve seen your paint spattered overalls and listened to your single-minded diatribes in dingy cafes. So can you tell me what the difference is between art and art-as-research? I get the feeling that art-as-research involves the production of knowledge, whatever that is. Whereas by-itself art is, you know, an indulgence of the self. Anyway, I figured that since you weren’t around to clarify this, I’d just Google “define:research”. The cloud-mind told me that Research was a full-rigged sailing ship built in 1861, renowned for the voyage it made from Quebec to Glasgow in 1866, during which its rudder was torn off by a Turner-esque storm-as-sublime in theAtlantic. The plucky captain and his loyal crew set about jury-rigging another rudder, only to have that one destroyed as well. Anyway, to cut a long story short, the crew kept on dismantling bits of the boat in order to fabricate a working rudder, but the implacable seas kept on tearing their constructions to pieces. In the end they made it to Glasgow, but the ship had to be towed into port and it looked more like a Venetian gondola than a cargo vessel since they’d had to produce eight different rudder versions on the way from whatever was available. So I guess art-as-research is something along those lines…’
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".