Bibliographic record
Abstract
Les mobilisations des diasporas africaines dans plusieurs territoires, si manifestes durant la pandémie, apparaissent aujourd’hui comme des leviers pour relever les défis contemporains d’un monde en crise. L’expérience diasporique, et l’identité relationnelle qui la porte, constitue l’un des clés de la réinvention d’un développement fondé sur la solidarité. Compte rendu du débat en ligne conçu par le Musée national de l’histoire de l’immigration en partenariat avec l’Agence française de développement (AFD), dans le cadre du cycle Le Musée part en live ! Animé par la journaliste Nora Hamadi, le débat réunit Alain Mabanckou, écrivain, professeur, titulaire de littérature francophone à l’Université de Californie à Los Angeles ; Hélène N’Garnim-Ganga, avocate, directrice du département Transition politique et citoyenne de l’Agence française de développement ; Sokona Niakhaté, maire adjointe de Fontenay-sous-Bois, conseillère départementale du Val-de-Marne, présidente de la Coordination des élus français d’origine malienne (Cefom).
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".