Bibliographic record
Abstract
Substance use and misuse is a significant global health issue that requires a sex- and gender-based analysis. Substance use patterns and trends are gendered: that is, women and men, girls and boys, and gender-diverse people often exhibit different rates of use of substances, reasons for use, modes of administration, and effects of use. Sex-specific effects and responses to substances are also important, with various substances affecting females and males differentially. Nevertheless, much research and practice in responding to substance use and misuse remains gender blind, ignoring the impacts of sex and gender on this important health issue. This special issue identifies how various aspects of sex and gender matter in substance use, illustrates the application of sex- and gender-based analyses to a range of substances, populations and settings, and assists in progressing sex and gender science in relation to substance use.
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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".