Using sustainability as a collaboration magnet to encourage multi-sector collaborations for health
Bibliographic record
Abstract
The World Health Organization Commission on Social Determinants of Health (SDH) places great emphasis on the role of multi-sector collaboration in addressing SDH. Despite this emphasis on this need, there is surprisingly little evidence for this to advance health equity goals. One way to encourage more successful multi-sector collaborations is anchoring SDH discourse around 'sustainability', subordinating within it the ethical and empirical importance of 'levelling up'. Sustainability, in contrast to health equity, has recently proved to be an effective collaboration magnet. The recent adoption of the Sustainable Development Goals (SDGs) provides an opportunity for novel ways of ideationally re-framing SDH discussions through the notion of sustainability. The 2030 Agenda for the SDGs calls for greater policy coherence across sectors to advance on the goals and targets. The expectation is that diverse sectors are more likely and willing to collaborate with each other around the SDGs, the core idea of which is 'sustainability'.
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 | 0.083 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.048 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".