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Record W4386033259 · doi:10.19088/ids.2023.041

L’application des connaissances dans les pays du Sud : Établir des liens entre les différents systèmes de connaissances pour un développement équitable

2023· report· fr· W4386033259 on OpenAlexfundno aff
James Georgalakis, Fajri Siregar

Bibliographic record

Venuenot available
Typereport
Languagefr
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
FundersEconomic and Social Research CouncilUniversiteit van AmsterdamUniversitas IndonesiaForeign, Commonwealth and Development OfficeUniversity of BathInternational Development Research Centre
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cette étude examine l’application des connaissances (AC) dans les pays du Sud et fournit des recommandations aux donateurs afin de mettre en place des structures et des stratégies plus efficaces dans le cadre de l’utilisation de la recherche pour un développement équitable. Le projet examine les stratégies, les pratiques et les théories de l’AC utilisées par les chercheurs et les médiateurs de recherche dans les pays du Sud, ainsi que les défis auxquels ils sont confrontés, et identifie les types de soutien requis de la part des organismes subventionnaires de la recherche. La conception des méthodes mixtes comprenait des sessions d’apprentissage dirigées, un examen de la documentation, la sélection et l’analyse d’études de cas ainsi que des entretiens semi-dirigés. La recherche indique que l’AC est définie de façon trop restreinte et qu’une approche globale est nécessaire pour soutenir sa mise en oeuvre dans les pays du Sud. Les recommandations à l’intention des organismes subventionnaires comprennent la création de fonds de défi, l’adaptation des programmes pour soutenir l’AC ainsi que la prise en compte de la complexité de chaque situation.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.162
GPT teacher head0.309
Teacher spread0.147 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicCommunity Development and Social ImpactFrench-language works237,207