Courtiers en développement et contrôleurs d’accès : deux figures incontournables des politiques de lutte contre la pauvreté rurale en Afrique du Sud
Bibliographic record
Abstract
Cet article analyse la mise en œuvre de programmes publics de lutte contre la pauvreté rurale en Afrique du Sud et fait le parallèle avec les politiques d’aide internationale au développement, en soulignant en particulier un de leurs travers, à savoir leur relative inefficacité. En remobilisant un concept-clé des travaux en anthropologie, celui du courtier , couplé à la redécouverte de l’idée de ≪ point de passage obligé » ( gatekeeping ) issue de la science politique, cet article montre à quel point les courtiers ont l’ambition de s’imposer comme des intermédiaires hégémoniques au sein d’un univers du courtage très disputé. À cette occasion, notre article s’interroge sur les relations entre les figures nouvelles et traditionnelles du courtage, c’est-à-dire entre les conseils d’administration des comités d’irrigation et les élites tribales.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Analysis of brokers and gatekeepers in South African rural anti-poverty programs; development policy.
This article analyzes rural-poverty programs and development policy rather than research practice.
Anthropology/political analysis of development brokers in rural South Africa; development policy, not metaresearch.
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".