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Record W1944144234 · doi:10.1002/jid.2870

RESEARCH CAPACITY‐BUILDING IN AFRICA: NETWORKS, INSTITUTIONS AND LOCAL OWNERSHIP

2012· article· en· W1944144234 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of International Development · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersInternational Development Research CentreWellcome TrustEconomic and Social Research CouncilBill and Melinda Gates Foundation
KeywordsCapacity buildingKey (lock)BusinessEconomic growthKnowledge managementPublic relationsPolitical scienceComputer scienceEconomicsComputer security

Abstract

fetched live from OpenAlex

Abstract Networked models are often proposed as a means to enhance health research capacity‐building in Africa. This paper addresses a knowledge gap on what works and does not in capacity‐building in African research settings. It provides an analysis of how multi‐partner networks are built and how their success depends on building institutional level capacity‐strengthening within partner institutions. To do this, the paper focuses on the Wellcome Trust's African Institutions initiative, drawing on initial learning and evaluation project data. We identify priority areas for policy attention and share emerging early insights on mechanisms and strategies being implemented by consortia to address key challenges. Copyright © 2012 John Wiley & Sons, Ltd.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.428
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.776
GPT teacher head0.656
Teacher spread0.120 · 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