Intersectoral policy on industries that produce unhealthy commodities: governing in a new era of the global economy?
Bibliographic record
Abstract
Tobacco, alcohol and unhealthy foods are key contributors to non-communicable diseases globally. Public health advocates have been proactive in recent years, developing systems to monitor and mitigate both health harms and influence by these industries. However, establishing and implementating strong government regulation of these unhealthy product-producing industries remains challenging. The relevant regulatory instruments lie not only with ministries of health but with agriculture, finance, industry and trade, largely driven by economic concerns. These policy sectors are often unreceptive to public health imperatives for restrictions on industry, including policies regarding labelling, marketing and excise taxes. Heavily influenced by traditional economic paradigms, they have been more receptive to industry calls for (unfettered) market competition, the rights of consumers to choose and the need for government to allow industry free rein; at most to establish voluntary standards of consumer protection, and certainly not to directly regulate industry products and practices. In recent years, the status quo of a narrow economic rationality that places economic growth above health, environment or other social goals is being re-evaluated by some governments and key international economic agencies, leading to windows of opportunity with the potential to transform how governments approach food, tobacco and alcohol as major, industry-driven risk factors. To take advantage of this window of opportunity, the public health community must work with different sectors of government to(1) reimagine policy mandates, drawing on whole-of-government imperatives for sustainable development, and (2) closely examine the institutional structures and governance processes, in order to create points of leverage for economic policies that also support improved health outcomes.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".