Review of: "The case for denicotinising tobacco in Aotearoa NZ remains strong: response to online critique"
Bibliographic record
Abstract
In brief, we reassert that the modelling is based on inappropriate extrapolations from a smoking cessation trial that bears no relation to the market conditions that would emerge following the denicotinisation mandate. Expert intuition cannot provide much more than arbitrary guesswork, as there is no experience to draw upon. On the basis of our critique, we do not argue that the denicotinisation policy should be dropped, only that the modelling is not informative for policymakers and may be falsely reassuring. We accept that a denicotinisation measure is likely to reduce smoking, but we also anticipate a range of adverse consequences and recommend that these are considered more carefully. We note recent official data showing that the current policy, based on user consent and encouragement to quit or to switch to lower-risk products, is proving very effective and already making progress well beyond the baselines used in the modelling. We recommend, therefore, that a stronger counterfactual is developed as the basis for comparison with the proposed denicotinisation measure. This could better reflect the observed effectiveness of the current policy and include further measures to accelerate voluntary switching from smoked to smoke-free nicotine products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".