Bibliographic record
Abstract
Room, R. (2016). Sources of funding as an influence on alcohol studies. The International Journal Of Alcohol And Drug Research, 5(1), 15-16. doi:http://dx.doi.org/10.7895/ijadr.v5i1.231When I first read Thomas Kuhn’s (1962) seminal work, shortly after its first publication, I was awakened to the historical evidence that even the “hardest” science is a human construction deeply influenced by the social order and the conceptual traditions in which the scientist works. On the other hand, as constructivism took hold in sociology, I realized I was a “soft” constructivist, willing to acknowledge that our conceptual and other constructions face some limits from the physical world and its operating rules (Room, 1984). But in fields like ours, the constraints are quite broad, so that what constitutes alcohol social science—what its research questions are, and how it approaches them—has varied a great deal over the last century or so, and varies considerably among the societies which have been willing to fund such research. I remember discovering that temperance-oriented survey studies, when they turned attention at all beyond the boundary between drinker and abstainer, focused only on frequency of drinking, ignoring amount per occasion (Lindgren, 1973)— a pattern found also in drug war–era drug surveys. What we collect as material for study and what we focus on in analyzing it are deeply influenced by our intellectual and cultural-political heritage and environment.
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 | 0.261 | 0.570 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".