Experimental analysis of behavior and tobacco regulatory research on nicotine reduction
Bibliographic record
Abstract
With the signing of H.R. 1256, the Family Smoking Prevention and Tobacco Control Act, the United States Food and Drug Administration (FDA) gained regulatory authority over the tobacco industry. A notable clause in this Act permits the FDA to regulate nicotine yields. However, they cannot completely remove this addictive constituent from tobacco products. This restriction has prompted the FDA to seek research on the threshold dose of nicotine that does not support dependence. This idea of threshold dose has led to an interesting reframing of scientific questions. For example, some researchers studying nicotine from this regulatory perspective translated the notion of an addiction threshold to a construct thought to play a role in addiction but which can be more readily operationalized. Examples include reinforcement threshold, discrimination threshold, and reinforcer-enhancement threshold. In this Perspective Paper, we highlight the importance of behavioral pharmacology and, specifically, the experimental analysis of behavior to help establish a scientific basis for policy decisions regarding nicotine yields. Recent research, including exemplars provided herein, note vast individual differences in the effects of nicotine at a known dose. Unfortunately, the behavioral and biological factors that contribute to such individual variations remain to be understood. We believe that behavior analysts are uniquely well-positioned to contribute to this understanding.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".