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Record W2799591365 · doi:10.1016/s2542-5196(18)30054-8

Redesigning public health for planetary health

2018· article· en· W2799591365 on OpenAlexaff
Astrid Brousselle, Camille Butzbach

Bibliographic record

VenueThe Lancet Planetary Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsHôpital Charles-Le MoyneUniversité de SherbrookeUniversity of Victoria
Fundersnot available
KeywordsPublic healthScopusPolitical scienceSocial determinants of healthPublic relationsHealth policyPoliticsMedicineMEDLINELawNursing

Abstract

fetched live from OpenAlex

The current state of planetary health inevitably leads to recognition of the failure of our current models of development, and it constitutes a call for each of us to profoundly revise our frameworks for action. The public health field, with its mission of protecting and promoting health and equity, is at the forefront of such an agenda. However, we are still ignoring the harmful effects of our current recommendations on planetary health. As practitioners and researchers, we need to invest in a new public health ecological approach.

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)0.092
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.488

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.111
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.004
Science and technology studies0.0070.034
Scholarly communication0.0210.042
Open science0.0060.034
Research integrity0.0200.027
Insufficient payload (model declined to judge)0.0390.012

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 head0.260
GPT teacher head0.370
Teacher spread0.111 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations12
Published2018
Admission routes1
Has abstractyes

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