Reconstructing Cross-Cultural Meanings of Addiction Among Women with Cultural Domain Analysis
Bibliographic record
Abstract
Exploring the cultural dimensions of addiction and recovery among marginalized populations presents significant challenges due to their “hard-to-reach” status and the complexity of measuring “culture.” This paper addresses these challenges by introducing and applying cultural domain analysis, a versatile method for systematically measuring cultural concepts within marginalized groups. Specifically, we use this approach to examine cultural models of addiction. The study was conducted in London, Toronto, and Delhi among reproductive-aged women receiving treatment for substance use disorders. Participants completed a semi-structured questionnaire featuring open-ended and free-list prompts. Findings revealed culturally specific themes at each site, highlighting insights often overlooked by purely quantitative methods. The analysis also uncovered cross-site similarities, such as the role of peer networks in recovery in both India and Toronto. When applied to hybrid data, these results demonstrate how cultural domain analysis provides a structured yet adaptable framework for identifying cultural differences and shared patterns. In conclusion, working with hard-to-reach populations necessitates flexible research methods that authentically amplify participants’ voices while maintaining methodological rigor. Cultural domain analysis achieves this balance, offering a systematic approach to capturing the salience of participants’ perspectives.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".