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Record W3167926352 · doi:10.1177/17579759211019214

Dismantling the status quo: promoting policies for health, well-being and equity: an IUHPE2022 prelude

2021· letter· en· W3167926352 on OpenAlexaffabout
Brittany Jock, Carole Clavier, Evelyne de Leeuw, Katherine L. Frohlich

Bibliographic record

VenueGlobal Health Promotion · 2021
Typeletter
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsStatus quoPublic relationsPolitical scienceRhetoricEquity (law)Health promotionUnrestEconomic growthGlobal healthGeopoliticsSociologyHealth careEconomicsLawPolitics

Abstract

fetched live from OpenAlex

The next international gathering of the global health promotion family will be in Montreal, in May 2022. The 24th IUHPE conference is themed 'Promoting policies for health, well-being and equity'. Conference organizers have decided to transcend the 'usual suspects' rhetoric and frame a conference program that truly challenges these key notions for health promotion. In this contribution, members of the Canadian National and Global Scientific Committees reflect on the state of play and the opportunities ahead. We propose three themes as follows: (a) breaking news (the promise and opportunities for disruptions and tipping points, whether from pandemic health challenges, climate change, geopolitical shifts, social unrest or technological promise); (b) breaking free (from world-views that favor only market solutions, divisions between North and South, toward emancipatory decolonizing practices and knowledge systems); and (c) breaking through (disciplines, silos, boundaries and identities engrained in our practices and understandings for innovation.).

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.355
Threshold uncertainty score0.707

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0240.013
Scholarly communication0.0110.007
Open science0.0030.004
Research integrity0.0710.061
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.064
GPT teacher head0.396
Teacher spread0.332 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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