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Record W1968143687 · doi:10.1080/09614520802386314

On the agenda: North–South research partnerships and agenda-setting processes

2008· article· en· W1968143687 on OpenAlexfundno aff
Megan Bradley

Bibliographic record

VenueDevelopment in Practice · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsObstacleGlobal SouthPublic relationsPolitical scienceBest practiceProcess (computing)General partnershipSection (typography)Public administrationEconomic growthSociologyBusinessGeographyEconomics

Abstract

fetched live from OpenAlex

Co-operation between researchers in the global North and South is critical to the production of new knowledge to inform development policies. However, the agenda-setting process is a formidable obstacle in many development research partnerships. The first section of this article examines how bilateral donor strategies affect collaborative agenda-setting processes. The second section explores researchers' motivations for entering into North–South partnerships; the obstacles that Southern researchers encounter in agenda-setting processes; and the strategies that they employ to ensure that research partnerships respond to their concerns. This analysis suggests that while strong Southern research organisations are best placed to maximise the benefits of collaboration, donors and researchers alike are well advised to recognise the limitations of this approach and use it prudently, because North–South partnerships are not necessarily the best way to advance research agendas rooted in Southern priorities.

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)0.106
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.561

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1060.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0160.031
Scholarly communication0.0250.022
Open science0.0020.030
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0090.001

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 head0.420
GPT teacher head0.360
Teacher spread0.060 · 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
DomainIncentives
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

Citations96
Published2008
Admission routes1
Has abstractyes

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