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Record W2778040965 · doi:10.20320/rfcsudes.v4i1.100

How have epidemiologists used the term community?

2017· article· es· W2778040965 on OpenAlexaff
Adriana Angarita Fonseca

Bibliographic record

VenueRevista Facultad de Ciencias de la Salud UDES · 2017
Typearticle
Languagees
FieldSocial Sciences
TopicPublic Health and Social Inequalities
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHumanitiesLogos Bible SoftwareArtPhilosophyTheology

Abstract

fetched live from OpenAlex

En conclusión, una comunidad es una subpoblación que comparte características comunes, y la epidemiología estudia poblaciones y subpoblaciones. Por lo tanto, los epidemiólogos casi siempre estudian comunidades. Hay muchas maneras en que los epidemiólogos han usado el término "comunidad". En este editorial, mencioné algunos usos epidemiológicos del término "comunidad", como los criterios de inclusión, la unidad de análisis, el análisis estadístico, la epidemiología basada en la comunidad y la vigilancia epidemiológica. Sin embargo, pueden existir otros usos que no he explorado aquí.

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)0.112
metaresearch head score (Gemma)0.278
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1120.278
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0110.011
Science and technology studies0.0060.025
Scholarly communication0.0210.033
Open science0.0050.009
Research integrity0.0160.019
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 head0.151
GPT teacher head0.444
Teacher spread0.293 · 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.

Study designQualitative
DomainMethods
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

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueRevista Facultad de Ciencias de la Salud UDESSame topicPublic Health and Social InequalitiesFrench-language works237,207