‘Radio as usual’? Digital technologies and radio in conflict-Affected Burkina Faso
Bibliographic record
Abstract
This article identifies four new realities shaping the ways in which Burkinabe radio journalists deal with the insecurity threat that emerged in 2015 and the rise of terrorism in the region, all of which are related to digital technologies. First, digital technologies may symbolically strengthen the collective radio journalist-listener link; second, digital technologies are also tools that help journalists and audiences face the challenges of the new security situation; and third, digital technologies can represent risks to journalists and listeners. But this research also highlights, fourthly, that digital technologies can be inappropriate, and that the security context is creating a new modernity for former—more traditional—uses of radio. These realities indicate that digital technologies are integral to the appropriating and modernising process affecting traditional modes of listening and reception. Drawing on 37 interviews and three focus groups with Burkinabe community radio journalists in 2022, the article discusses existing literature in the Global North that highlights the significant disruptive effect of digital technology on radio both as a device, and in terms of broadcasting and listening practices, with it being suggested that traditional FM radio’s very survival is threatened. It finally shows that whilst digital technologies might sound a death knell for traditional broadcasting formats in the Global North, suggesting an ‘either/or’ situation, the situation differs in Burkina Faso, and therefore in other similarly affected conflict zones, where digital technologies reconceptualise the use of traditional radio without threatening it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".