Bibliographic record
Abstract
Responding to the commentaries on a recent paper on the elusiveness of representativeness in general population alcohol surveys, we can summarise that there is agreement that the status quo of current alcohol surveys is scientifically no longer defensible. Current surveys cannot per se be assumed to yield representative results for the general populations of countries based on a probabilistic sampling alone. Alternatives are discussed and-as for any survey-creative ideas on validating key results on indicators or hypotheses need to be developed and used. This will inevitably lead away from omnibus surveys to more focused studies requiring more complex methodological tools. While there may not be obvious solutions for every problem related to alcohol use prevention and policy or treatment use disorders, and it may take years to find solutions for some of the issues, continued use of the methodology of the status quo will surely fail to answer the questions posed by modern societies concerning these issues.
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 | 0.160 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.033 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.051 | 0.027 |
| Insufficient payload (model declined to judge) | 0.023 | 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".