Getting from Analysis to Action: Framing Obesity Research, Policy and Practice with a Solution-Oriented Complex Systems Lens
Bibliographic record
Abstract
Public policy aimed at reducing obesity is just one of many avenues that must be pursued to address the still-growing obesity pandemic. The complexity of the problem is illustrated in ecological frameworks and system maps of the determinants. These conceptual maps illustrate the complexity by acknowledging the influence of many different factors such as social norms and values; sectors of influence such as the food and beverage industries, media and transportation; behavioural settings including home and family, school and community; and individual factors such as genetics, psychosocial and other personal elements. But to solve such a complex problem, we need to move from an analysis of the determinants or causes of the problem to a solution orientation; the frameworks used to describe the problem may not be the right ones for building the "best" solutions. Solution-oriented frameworks, like those presented by Hobbs and Seeman, have been based on parameters such as the sector of influence (e.g., public policy) but would benefit from the consideration of complexity and the leverage points for intervention in complex systems, which are a function of parameters such as the structure of relationships and the presence or absence of feedback loops.
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 | 0.065 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.101 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.080 | 0.090 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".