Gender-Responsive Approaches to the Acceptability, Availability and Affordability of Alcohol. Brief 11
Bibliographic record
Abstract
Gender-related norms persist in our societies, including in the consumption of alcohol. Despite knowing that men and women consume alcohol differently and are affected by its harm differently, alcohol control policies remain essentially gender blind. Highly gendered approaches to alcohol marketing and gender differences in patterns of alcohol consumption and its associated harm are well documented. Relatively little evidence has examined the different effects of population-level alcohol control policies on different genders, and even less has addressed how gender intersects with socioeconomic status, age, ethnicity, and other factors.<br/>Experiences from countries illustrate gendered approaches being used by the alcohol industry (Brazil and the United States) and the innovative ways that governments and civil society organizations are tackling the gendered effects of alcohol consumption. This includes promoting employment outside the alcohol industry (United Republic of Tanzania), developing gender-specific supports for alcohol consumption (Pakistan and Scotland), mobilizing civil society to enforce marketing bans (Sri Lanka) and creating culturally sensitive and culturally embedded policies (Aotearoa New Zealand). There is a clear need for policy-relevant research that supports an increased understanding of what works for gender-responsive approaches to reduce the harm caused by alcohol consumption.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".