Building Momentum in International Social and Epidemiological Research on Alcohol and Drugs: Continuing the Legacy of IJADR
Bibliographic record
Abstract
The International Journal of Alcohol and Drug Research (IJADR) is the official journal of the Kettil Bruun Society for Social and Epidemiological Research on Alcohol (KBS).In alignment with the Society's aims, the journal's objectives are to publish and promote social and epidemiological research on alcohol and foster a comparative understanding of alcohol use and alcohol problems internationally.The Journal also publishes papers focused on other drugs and addictive substances and has a history of soliciting and publishing papers on special issues that are likely to be of interest to its readership.Since its inception, IJADR has also sought to highlight culturally diverse views on alcohol and other drug problems, and provide a specific outlet for research from low and middle income countries.It seeks to support and publish qualitative and mixed methods papers, in addition to quantitative studies, and our strong senior editorial team reflects that capacity.IJADR has also been able to address the gender imbalances in addiction journals, as have been indicated in a paper by Mathilda Hellman (2020), as women are well represented on our editorial team.
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 | 0.200 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.059 | 0.034 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.020 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".