Bibliographic record
Abstract
Abstract The Ottawa Charter for Health Promotion was launched in 1986, and ten global conferences later its key calls to action have never been more poignant. To see health as a resource for everyday life in settings where people live, love, work and play, and recognizing equity and the social determinants of health as core to the success of healthy societies remains important. In the nearly 35 years since the Charter was published, we have seen a proliferation of health promotion research with ever greater insights in what drive the health and well-being of populations. Yet, at the same time we also witness a strong tendency to ground health (care) policy in biomedical and clinical evidence alone, and attribute health potential to lifestyle alone, rather than adopting a systems and social perspective of where health is created, grown, and celebrated. The causes for these diverging perspectives are complex, and are grounded in complexity. Humans and their socio-political systems, including the educational and political machines, tend to suffer from what the political scientist Charles Lindblom reputedly identified as the “Big Problem, Small Brain vs Small Problem Big Brain” phenomenon: researchers and intellectual are really good at pouring great volumes of thought and creative power into studying clearly defined issues, whereas politicians and bureaucrats face enormous problems and can get their heads around the multi-faceted solutions that need to be put in place. So - how do we make the complex palatable to the small brain? In this case - how can higher education systems be turned around to truly address the challenges of our and our children's time? The solution partially lies in the deployment of multiple network analyses of key stakeholders and the language they use to construct future realities: unless we have a clear map of the present and a much wider terrain before us to enter we will forever find it hard to navigate in the environmental and conflict dimension.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 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".