Where is the Science? A Critical Interrogation of How Sex and Gender are Used to Inform Low-Risk Alcohol Use Guidelines
Bibliographic record
Abstract
: Across the globe, many countries publish low-risk alcohol guidelines which outline the recommended best practices for drinking limits to reduce the health risks and harms associated with excessive alcohol use. Frequently, low-risk drinking guidelines include different recommendations for cisgender men and women. As researchers working in the area of trans-inclusive substance use treatment and care, we are interested in the rationale for how gender-based low-risk drinking guidelines are determined, including the role of evidence and science. We argue that low-risk drinking guidelines based on sex and/or gender are highly insufficient and not engaging with a robust evidence base, and we further argue that it is important that we attend to these concepts correctly as we develop clinical and public health guidelines, which will undeniably have an impact on the individuals and societies who rely on them.
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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 | 0.400 | 0.559 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.014 | 0.099 |
| Scholarly communication | 0.031 | 0.050 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.027 | 0.062 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".