Bibliographic record
Abstract
INTRODUCTION: As considered in the rest of this volume, the effects of the IDEFICS intervention on obesity rates were not encouraging. This paper considers how far findings from the IDEFICS study and similar intervention studies are relevant to the policy process and political decision-making. METHODS: The paper offers theoretical and policy-level arguments concerning the evaluation of evidence and its implications for policymaking. The paper is divided into three parts. The first considers problems in the nature and applicability of evidence gained from school- and community-level obesity interventions. The second part considers whether such interventions present a model that policymakers could implement. The third part considers how we should think about policy measures given the limited evidence we can obtain and the many different goals that public policy must take account of. RESULTS: The paper argues that (1) there are clear reasons why we are not obtaining good evidence for effective school- and community-level interventions; (2) public policy is not in a good position to mandate larger-scale, long-term versions of these interventions; and (3) there are serious problems in obtaining 'evidence' for most public policy options, but this should not deter us from pursuing options that tackle systemic problems and have a good likelihood of delivering benefits on several dimensions. CONCLUSIONS: Research on school- and community-level obesity interventions has not produced much evidence that is directly relevant to policymaking. Instead, it shows how difficult it is to affect obesity rates without changing wider social and economic factors. Public policy should focus on these.
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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 | 0.035 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".