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Record W2604459421 · doi:10.17605/osf.io/y65jv

Ces experts qui venaient du froid : Comment des Canadiens deviennent experts en développement de l'Afrique subsaharienne

2018· article· en· W2604459421 on OpenAlexaboutno aff
Laurent Paradis-Charette

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Each year, hundreds of Canadian experts offer their opinions, their skills, their knowledge or know- how to implement, monitor and evaluate projects and programs aimed at developing communities in the Sub-Saharan African region. Canada is a northern country located roughly 5000 km from the nearest African shore, therefore it becomes logical to ask how foreigners can become experts in the development of African communities. Using Bourdieu's field theory, we analyze the interviews gathered in this research to show that experts must be able to accumulate, mobilize, trade or transform technological, organizational, social and expertise capitals specific to the western world. Because of that, the field of expertise in international development is in a constant struggle to reaffirm its autonomy next to the dominant fields of the western world notably the political and economical fields. /// Chaque année, des centaines d'experts canadiens offrent leurs avis, leurs compétences, leurs connaissances ou leurs savoirs-faire, afin de mettre en place, de suivre et d'évaluer des projets et des programmes destinés à développer des communautés d'Afrique subsaharienne. Le Canada étant un pays nordique situé au minimum à plus de 5000 kilomètres des côtes africaines, il devient logique de se demander comment des étrangers venus de si loin sont devenus experts du développement des communautés africaines. À partir de la théorie du champ de Bourdieu, l'analyse des entretiens menés dans le cadre de cette recherche nous indique que, pour devenir expert, il faut être en mesure d'accumuler, de mobiliser, d'échanger ou de transformer des capitaux technologiques, organisationnels, sociaux et d'expertises spécifiques à l'occident. En ce sens, le champ de l'expertise en développement international se trouve en lutte constante pour sauvegarder son autonomie vis-à-vis des champs qui dominent la société occidentale, notamment le champ économique et le champ du pouvoir.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2490.086

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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