Bibliographic record
Abstract
As a brand-new undergraduate, Tony Nguyen wasn’t sure what type of science he wanted to study. Should he major in chemistry? Physics? Something else? Fortunately, he didn’t have to decide. Nguyen joined the inaugural cohort of the University of Western Ontario’s integrated science program, one of a handful at universities in Canada and the UK that are treating the sciences as a whole rather than a bunch of siloed parts. “The integrated science program attracted me because I was able to be exposed to all the sciences and basically choose the ones I liked the most,” says Nguyen, who eventually picked chemistry and will graduate next spring. This is not the first time universities have offered this type of wide-ranging program. But previous integrated science programs have focused on introductory coursesrather than entire degrees. Derek Raine, a physicist at the University of Leicester, developed the first of this new variety
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 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".