Manufacturing Conflict? An Ethnographic Study of the News Community in Abidjan, Côte dâIvoire
Bibliographic record
Abstract
This ethnographic study explores the experiences of Ivorian journalists in the context of the 2002-2009 crisis in Abidjan, the economic capital city of Côte d’Ivoire. I present material on the political affiliations of newspapers, the structure of the news industry, the attitudes of journalists, and certain aspects regarding the reception and dissemination of media texts in the streets of Abidjan. My interests lie in analysing the origins and the impacts of the accusations to which journalists of the written press are being subjected concerning their role in the Ivorian conflict. I explore how the crisis has been constructed and construed by and through media agents. I focus on the reflexive moments of journalists and on what their metadiscourses reveal about the context of news production in Côte d’Ivoire. Data was collected through participant-observation and interviews over 18 months of fieldwork in 2003, 2004-2005 and 2006 mainly in three newsrooms in Abidjan. This dissertation questions the emphasis placed upon the role of media in African conflicts, which I term the Rwandan paradigm. The Rwandan paradigm is the reductionist notion that mass media indoctrination plays a decisive role in mobilizing African audiences to commit acts of communal violence. Ultimately, I suggest two avenues to broaden our understanding of the intersection between communication and conflict: 1) a recognition of the complex agency of media producers and their audience; 2) an exploration of alternative media and public spaces.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".