Bibliographic record
Abstract
Obesity is clearly a complex problem for both the individual and for society. Complex or 'wicked' problems have common characteristics such as heterogeneity, nonlinearity, interdependence, and self-organization. As such they require solutions appropriate for complex problems, rather than a reductionist search for the causes. 'Systems thinking' provides new ways to consider how to collectively address complex societal problems like obesity, where biology interacts with social, cultural and built environmental factors in infinite permutations and combinations. The systems that give rise to the obesity epidemic function at multiple levels, and there are important interactions between these levels. At any given level, individual actors and organizations matter and system function is optimized when individual and organizational capacity to respond is well matched to the complexity of individual tasks. Providing system supports to help networks of individuals become 'communities of practice' and 'systems of influence' may also help to accelerate the pace of effective action against obesity. Research efforts need to move away from the relentless search for the specific isolated causes of obesity and focus on solutions that have been shown to work in addressing other 'wicked' problems.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".