Bibliographic record
Abstract
Unrecorded alcohol consumption is a global problem, with about 25% of all alcohol consumption concerning this category. There are different forms of unrecorded alcohol, legally produced versus illegally produced, artisanal vs industrially produced, and then surrogate alcohol, which is officially not intended for human consumption. Monitoring and surveillance of unrecorded consumption is not well developed. The World Health Organization has developed a monitoring system, using the Nominal Group Technique, a variant of the Delphi methodology. Experiences with this methodology over the past two years are reported. Finally, conclusions for the monitoring and surveillance at the national level are given. Nierejestrowana konsumpcja alkoholu, której udział w spożyciu alkoholu ogółem wynosi około 25%, jest problemem o charakterze globalnym. Istnieje wiele źródeł konsumpcji nierejestrowanej, takie jak produkcja legalna i nielegalna, rzemieślnicza i przemysłowa, a także alkohol, który oficjalnie nie jest przeznaczony do spożycia. Monitorowanie tej konsumpcji jest słabo rozwinięte. Dopiero ostatnio, Światowa Organizacja Zdrowia wypracowała system monitoringu oparty na Nominalnej Technice Grupowej (Nominal Group Technique), która mieści się w szerszej kategorii metodologii delfickiej. Artykuł przedstawia doświadczenia z zastosowaniem tej metodologii zebrane w ciągu ostatnich dwóch lat oraz propozycje krajowego monitoringu konsumpcji nierejestrowanej.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".