Introduction to IJADR Volume 7, Issue 2
Bibliographic record
Abstract
Dear Readers, The editors of the International Journal of Alcohol and Drug Research (IJADR) are pleased to announce the release of the second issue of our 7th volume. This issue contains five research papers focused on a wide range of substance use topics such as family member’s satisfaction with alcohol and other drug treatment, risk factors for fractures in pre-menopausal drinkers who are alcohol-dependent, and the risk reduction potential of heat-not-burn tobacco products. The issue also includes a methodological description of the multinational GENAHTO (Gender and Alcohol’s Harms to Others) project. These papers represent research collaborations occurring in Brazil, New Zealand, Canada, Russia, England, and Germany, to name a few. Thank you for your interest. Please consider submitting your alcohol and drug focused research to the IJADR. Mark B. Reed, Ph.D. Co-Editor-in-Chief San Diego State University, School of Social Work, United States Samantha Wells, Ph.D. Co-Editor-in-Chief Centre for Addiction and Mental Health (CAMH), Institute for Mental Health Policy Research, Canada Sandra Kuntsche, Ph.D. Co-Editor-in-Chief Centre for Alcohol Policy Research (CAPR), School of Psychology & Public Health, La Trobe University, Australia
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.272 | 0.171 |
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".