Overview: <scp>B</scp>ellagio <scp>C</scp>onference on <scp>P</scp>rogram and <scp>P</scp>olicy <scp>O</scp>ptions for <scp>P</scp>reventing <scp>O</scp>besity in the <scp>L</scp>ow‐ and <scp>M</scp>iddle‐<scp>I</scp>ncome <scp>C</scp>ountries
Bibliographic record
Abstract
The Bellagio 'Conference on Program and Policy Options for Preventing Obesity in the Low- and Middle-Income Countries' (LMICs) was organized to pull together the current. We need not reiterate the importance of this topic or the speed of change in eating, drinking and moving facing us across the globe. The conference emerges from need to significantly step up the policies and programs to reduce obesity by learning from some current examples of best practice and strengthening the role of the academic and civil society players in translating global evidence and experience into action at the national level. There is also a need to empower the younger generation of scholars and activists in these countries to carry on this effort. The meeting was also timely because a number of funding agencies in the United States, Canada and the UK, at least, are beginning to focus attention on this topic. This set of papers provides not only examples of existing best practice but also a road map ahead for LMICs in the various areas of action needed to reduce obesity across LMICs. The meeting highlighted critical barriers to implementation that have blocked many initiatives.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.329 | 0.189 |
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".