Bibliographic record
Abstract
Recently as I was strolling through my neighborhood in Montreal, I came across a toy I saw that kids played with in the 1980s: the Cozy Coupe car with a bright yellow body and a red foot-powered chas-sis, a popular toy around that time. I remembered how the car’s cockpit allowed interfacing with familiar surroundings while giving a first sense of leg-powered, seated motility outside the house.In a contemporary videogame, by contrast, motility is delegated through the interface to avatars. One would be quick to think that the issue of delegation or that of the materiality of the interaction through the interface has gone from physical, bodily affect (rep-resented by the 1980s Cozy Coupe, among other toys) to that of an increasingly dematerialized network of programmed bodies in games. But the transition can’t be so easily reduced to this. For example, Ingmar Riedel-Kruse and his team at Stanford University have designed a new set of videogames, this time involving the use of living microorganisms instead of electronically programmed sprites or avatars.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.273 | 0.099 |
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".