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Record W2601740232 · doi:10.1177/1757975916683387

Using sustainability as a collaboration magnet to encourage multi-sector collaborations for health

2017· article· en· W2601740232 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Promotion · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of CalgaryUniversity of Ottawa
Fundersnot available
KeywordsSustainabilitySustainable developmentBusinessPublic relationsFraming (construction)Equity (law)Political scienceEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.470
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it