Mapping and comparing French people’s positions regarding restrictive control policies: a pilot study
Bibliographic record
Abstract
BACKGROUND: Public authorities resort to various control policies in order to curb the prevalence of unhealthy behaviors. As these policies can only succeed to the extent that people agree with them, this study mapped French people's positions regarding restrictive control policies in general. METHOD: A sample of 344 adults (among them health professionals and lawyers) were presented with 54 vignettes depicting a control policy. Each vignette contained four pieces of information: the type of addictive behavior targeted (smoking, drinking, or gambling), the nature of preventive measures (e.g., information campaigns), the degree of regulative measures (e.g., prohibition to minors), and the severity of sanctions. RESULTS: Through cluster analysis, eight qualitatively different positions were found: Never acceptable (9%), Weak or moderate regulation (5%), Moderate regulation associated with strong prevention (11%), Strong or moderate regulation (11%), Strong regulation in association with strong prevention (23%), Moderate sanctions in association with strong prevention and moderate regulation (9%), Severe sanctions (9%), and Always acceptable (9%). Some participants (14%) expressed no opinion at all. CONCLUSION: French people's positions regarding control policies were extremely diverse. Regarding tobacco, however, one type of policy would likely be supported by a majority of people: Moderate regulation associated with at least a moderate level of prevention and low-level sanctions. Regarding alcohol, an acceptable position would be: Moderate regulation associated with at least a moderate level of prevention and high-level sanctions. Regarding gambling, an acceptable position would be: Strong regulation associated with at least a moderate level of prevention and low-level sanctions.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".