Triggering change – How investigative journalists in Sub-Saharan Africa contribute to solving problems in society
Bibliographic record
Abstract
This article analyses 12 cases of investigative journalism in Sub-Saharan Africa. The reporters all claimed to have contributed to change processes by influencing government policy, action by state administration, supporting the uptake of scientific solutions or provoking public debate. An assessment of these processes shows that in 10 cases, the journalists indeed helped to trigger change and in two cases they failed to do so. The cases are evaluated through an explorative approach inspired by the dynamic models for communication on public issues developed by Rucht and Peters. Different types of investigative stories in Sub-Saharan Africa are identified and hypotheses are developed on key factors that were important in investigating and publishing the stories as well as in achieving change. A decisive element of investigative journalism in Sub-Saharan Africa seems to be the involvement of and the interaction with other societal non-journalist actors.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".