Embrace the Smoker: Person-First Language Is not a Solution to Stigma
Bibliographic record
Abstract
The recent editorial, “Time to Stop Using the Word ‘Smoker’: Reflecting on the Role of Language in Advancing the Field of Nicotine and Tobacco Research,”1 raises a number of important points about the use of language in nicotine and tobacco research. Language can indeed be important, and it is crucial to strive for precision in definitions and to use respectful terminology. However, I respectfully disagree with the call for person-first language as a solution to either of these issues. While the push for person-first language comes from a noble place, this terminology ultimately re-enforces stigma and may be actively harmful. The push for person-first language emerges from a set of real problems. As succinctly explained in the editorial,1 the historical terminology used to identify substance users was actively problematic. Terms were either simply slurs (ie, junkie), incorrect (“addict”--most users of most psychoactive substances are not addicted to or dependent on the substance) or both. It was unquestionably wrong, morally and scientifically, to use those terms. Stigma and stigmatizing causes real harms in an individual’s ability to address their substance use and harms associated with it as well as limiting the ability of public health and medicine to implement effective treatment and harm reduction.2,3
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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 | 0.008 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.081 | 0.022 |
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".