Bibliographic record
Abstract
Statistics, the much-maligned discipline feared by graduate students, is usually far removed from biomedical scientists’ day-to-day concerns. Possible exceptions are grant writing, addressing seemingly nitpicking journal reviewer comments, and seeking expert advice when experimental results are not as expected. Although biostatisticians see themselves as scientists in their own right, other scientists often see them as service providers or, worse, as impediments to getting the job done. Along these lines, US National Cancer Institute statistician Lisa McShane remarked how a biomedical scientist might expect a request for data analysis help to proceed:The statistician will probably tell me it will take six weeks to complete the analysis and might start asking questions about whether I carefully designed my study with appropriate controls and safeguards against confounding effects.
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 | 0.076 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.039 |
| Insufficient payload (model declined to judge) | 0.026 | 0.023 |
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".