Bibliographic record
Abstract
Sulkunen, P. (2016). Commentary: Knowledge is power, and power needs knowledge. The International Journal Of Alcohol And Drug Research, 5(1), 11-12. doi:http://dx.doi.org/10.7895/ijadr.v5i1.225In the years 2011–2015 I served on the expert committee of the foundation called ERAB, now a public charity, funded by the Brewers of Europe. Our task was to review grant applications to deliver half a million euros per year to research on alcohol consumption. There was no detectable indication that the proposals were biased to please the brewers, and certainly the decisions were not. However, things changed in 2015, when the Secretary General of the Brewers of Europe requested on behalf of the funders that 40% of the grant money should go to research aiming at “a better understanding of the effects of moderate beer consumption on the behaviour and health of individuals and society.” This was unacceptable to all experts on the committee, and the Chair of the Board of ERAB resigned in protest. After a battle between the Brewers and the expert committee, the formulation laid down was “more research on beer.” I could not accept this either, arguing that any external criteria beyond the quality of the proposals undermined the scientific integrity of the selecting experts.
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 | 0.021 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.092 | 0.094 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".