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Record W2094642875 · doi:10.1371/journal.pmed.0040273

Grand Challenges in Global Health: Community Engagement in Research in Developing Countries

2007· article· en· W2094642875 on OpenAlexfundno aff
Paulina Tindana, Jerome Amir Singh, C. Shawn Tracy, Ross Upshur, Abdallah S. Daar, Peter Singer, Janet Frohlich, James V. Lavery

Bibliographic record

VenuePLoS Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchU.S. Department of Health and Human ServicesNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates FoundationUniversity of Toronto
KeywordsCommunity engagementDeveloping countryConceptual frameworkKey (lock)Public relationsSociologyMedicinePolitical scienceComputer scienceSocial scienceEconomic growth

Abstract

fetched live from OpenAlex

The authors argue that there have been few systematic attempts to determine the effectiveness of community engagement in research.

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)0.211
metaresearch head score (Gemma)0.276
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2110.276
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0060.024
Scholarly communication0.0210.019
Open science0.0030.021
Research integrity0.0160.013
Insufficient payload (model declined to judge)0.0070.001

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 head0.506
GPT teacher head0.502
Teacher spread0.004 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
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

Citations419
Published2007
Admission routes1
Has abstractyes

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