Exploring vector space: overcoming resistance to direct control of the tobacco industry
Bibliographic record
Abstract
Within the epidemiological framework that describes the relationship of smokers (host), cigarettes (agent), tobacco companies (vector) and environment,1 both the agent and the vector are man-made and, in theory, controllable. Nonetheless, the smoking pandemic is expected to claim one billion lives in this century, even among people who are not yet born.2 With a preventable problem that is not being prevented, our disease strategy is arguably in need of a rethink. While health science has focused on establishing the link between tobacco products and the diseases they cause, treating those diseases, exploring ways to make cigarettes less harmful and ways to discourage tobacco use, relatively little health research has been focused on analysing the vector of the disease or how to change its course.3 This may explain why there is a global consensus to modify the behaviour of the host, environment and agent, but little pressure in support of vector control.4 Despite opposition to such supply-side approaches,2 5 a number of new vector-related tobacco control measures have been proposed. These include performance-based regulations,6 ending the manufacturer's obligation to increase or at least maintain shareholder value,7 regulating profits,8 banning some or all tobacco products or prohibiting use by some …
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".