Bibliographic record
Abstract
Stigma continues to be the largest barrier for accessing treatment among people experiencing drug addiction. The dominant portrayals that exist about people who use drugs are often damaging and act to dehumanize the group as a whole. When left unchallenged, stereotypes can act as truthful depictions and facilitate the resistance against harm reduction services that are based on a human rights model. The use of labels is one way stigma is perpetuated by eliciting the label's stereotyped narratives onto an individual or group. Within harm reduction discourse, the word "addict" can have detrimental effects on how the public perceives people experiencing addiction and their deservingness of pragmatic services. This article aims to draw attention to the inattention we give "addict" in language and explain how its routine use in society acts to perpetuate addiction stigma. Using the example of supervised injection site opposition in Canada, the use of "addict" is used as a way to understand how stigma through language works to impede the expansion of harm reduction initiatives.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".