L'image de l'Afrique dans les médias occidentaux : une explication par le modèle de l'agenda-setting
Bibliographic record
Abstract
Professeur Departement d’information et de communication Universite Laval (Quebec) La mauvaise representation de l’Afrique dans les medias occidentaux n’est ni un phenomene nouveau, ni un phenomene exceptionnel. Elle fait l’objet de preoccupations depuis les annees 1970, notamment dans le cadre des discussions sur le Nouvel ordre mondial de l’information et de la communication (NOMIC). Par ailleurs, on constate que toutes les minorites visibles ou communautes culturelles se plaignent souvent de la distorsion de leur image par les medias. Mais l’image que propagent les medias occidentaux de l’Afrique est d’autant plus preoccupante qu’elle influe negativement sur les efforts de developpement de l’Afrique. Le present article a pour objectif de rappeler un certain nombre de criteres qui president a la selection ou a la construction de l’information ayant trait a l’Afrique dans les medias occidentaux. Tout en tenant compte des raisons ideologiques (ethnocentriques) ou commerciales avancees par certains auteurs, l’article tente d’expliquer autrement cette problematique en evoquant l’impossibilite ou l’incapacite des sources africaines a influencer ou, tout au moins, a contribuer a l’« agenda » des medias occidentaux sur l’Afrique.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".