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Record W2889574615 · doi:10.5888/pcd15.180026

Visualizing Reach of Racial and Ethnic Approaches to Community Health for Asian Americans: the REACH FAR Project in New York and New Jersey

2018· article· en· W2889574615 on OpenAlexfundno aff
Susan S. Kum, Shilpa Patel, Mary Joy Garcia, Sara S. Kim, Rucha Kavathe, Catherine Ceniza Choy, MD Taher, Stella S. Yi, Nadia Islam, Simona C. Kwon

Bibliographic record

VenuePreventing Chronic Disease · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
FundersNational Center for Chronic Disease Prevention and Health PromotionNational Institute of Diabetes and Digestive and Kidney DiseasesCenters for Disease Control and PreventionYork UniversityNational Institute on Minority Health and Health DisparitiesNew York State Department of HealthState of New Jersey Department of HealthNational Center for Advancing Translational SciencesNew York City Department of Health and Mental HygieneNational Institutes of Health
KeywordsMedicineEthnic groupPublic healthHealth promotionDiseaseChronic diseasePromotion (chess)GerontologyFamily medicineNursingPathology

Abstract

fetched live from OpenAlex

Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.432
GPT teacher head0.479
Teacher spread0.047 · 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 designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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