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Record W4321612427 · doi:10.1093/gerona/glad065

Geroscience and Public Health’s <i>Plastic</i> “Ecology of Ideas”

2023· article· en· W4321612427 on OpenAlexaff
Colin Farrelly

Bibliographic record

VenueThe Journals of Gerontology Series A · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsQueen's University
Fundersnot available
KeywordsSanitationPublic healthEcologyEnvironmental ethicsPopulationControl (management)SociologySocial scienceGerontologyEnvironmental healthMedicineBiologyManagementPathologyPhilosophy

Abstract

fetched live from OpenAlex

In his 1910 JAMA address, the physician and pathologist Christian Herter (1865-1910) emphasized the importance of plasticity in science. Herter's insight is significant for understanding how public health's "ecology of ideas" must evolve and change as the health challenges facing populations alter through the different stages of "epidemiologic transition". The foundational moral aspiration (ie, disease control) and intellectual suppositions (eg, that public health is "purchasable") of the early twentieth-century public health pioneers C.-E.A Winslow (1877-1957) and his mentor Hermann Biggs (1859-1923) were shaped by sanitation science and were deployed to mitigate the risks of early-life mortality. But to meet the health challenges of today's aging world, public health's "ecology of ideas" must be plastic, and thus open to revision and refinement in terms of both its foundational moral aspirations and the intellectual suppositions concerning how to best improve population health. More medical research is needed in rate (of aging) control versus disease control.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.053
Scholarly communication0.0090.008
Open science0.0010.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.158
GPT teacher head0.367
Teacher spread0.209 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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Same venueThe Journals of Gerontology Series ASame topicClimate Change and Health ImpactsFrench-language works237,207