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Record W3000100016 · doi:10.1177/1028315319896756

Policy Ideas and North–South Research Cooperation: The Case of Norway

2020· article· en· W3000100016 on OpenAlexaff
Julian Weinrib, Creso M. Sá

Bibliographic record

VenueJournal of Studies in International Education · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNorwegianGeneral partnershipMainstreamReputationContext (archaeology)Global SouthPerspective (graphical)Regional sciencePolitical scienceSociologyPublic relationsSocial scienceEconomic geographyEconomicsGeography

Abstract

fetched live from OpenAlex

Geopolitically powerful actors in countries linked to the global North have historically shaped the landscape of North–South research cooperation. The literature documents not only the pervasiveness of asymmetrical relationships in North–South research cooperation but also a growing recognition among policy and academic actors of these dynamics. In this context, this study traces the invention of the Norwegian “South-South-North” partnership model, which was envisaged as an alternative to mainstream approaches and sought to foreground the needs and priorities of researchers in the global South. Employing the conceptual perspective of policy ideas, this study identifies three ideas that were constitutive of the model: humanitarianism, enlightened self-interested, and international reputation building. Functioning as broad public philosophies, these ideas underpinned the adoption of the South–South–North model as a distinctively Norwegian approach to research capacity building in the global South.

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)0.070
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0250.035
Scholarly communication0.0160.011
Open science0.0020.021
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.185
GPT teacher head0.512
Teacher spread0.327 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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