Stereotypes of Addiction in the United States: A Descriptive and Experimental Approach to Identifying Addiction Stereotypes and the Effectiveness of Positive-Counterstereotype Stigma-reduction Tactics
Bibliographic record
Abstract
Millions of individuals worldwide are affected by addiction, and stigma has been identified as a major barrier to recovery. However, the addiction literature has primarily researched addiction stigma broadly, with little information about distinct, drug-specific perceptions that permit the development of tailored interventions. This thesis extends the research by developing a comprehensive catalogue of perceptions of drug addictions and further examined one way these descriptions can be leveraged for social good. Across four studies (N = 451), I developed an adjective list appropriate for use with substance use disorder research (Pilot study). This list was used to catalogue the most stereotypical and counter-stereotypical adjectives associated with 14 drug categories (Study 1a), and further classify them according to valence (Study 1b). Finally, I tested whether a tailored, positive counter-stereotypic intervention was effective in reducing negative perceptions towards people addicted to cocaine (Study 2). The downstream consequences of these findings are discussed.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".