S’inspirer du journalisme environnemental pour produire un podcast en Afrique : cas de « La fin du mois et la fin du monde »
Bibliographic record
Abstract
Cet article amorce une réflexion sur la contribution de l’Afrique à la question primordiale de conciliation des impératifs de vie décente à court terme et les exigences écologiques vitales pour la survie de la planète. Pour le dire autrement en reprenant la formule devenue célèbre, comment concilier en Afrique les préoccupations de fin du mois et celles de fin du monde ? L’écologie sociale, qui postule que les problèmes écologiques découlent des problèmes sociaux, cherche à résoudre conjointement ces deux questions. Le choix est fait à dessein d’initier cette réflexion de manière originale avec la cocréation d’un podcast en Afrique, un continent souvent présenté comme ayant d’autres priorités que celles qui sont environnementales.
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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".