Approaches to triangulation of alcohol data in Scotland: Commentary on Rehm <i>et al</i>.
Bibliographic record
Abstract
Rehm et al. highlight the ongoing difficulties in accurately estimating alcohol consumption using surveys. Population surveys, in particular, suffer from non-response and sampling bias, which affects their representativeness, but they are one of the few ways of estimating differences in consumption across population subgroups. In this article, we highlight different approaches that have been taken in Scotland to try to overcome these problems, from the pragmatic to the sophisticated.
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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 | 0.060 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.054 | 0.050 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".