COVID-19 narratives and counter-narratives in Ghana: The dialectics of state messaging and alternative re/de-constructions
Bibliographic record
Abstract
As part of its efforts to manage the pandemic, the government of Ghana has tried to control messaging via press conferences, only to find out that it has to contend with a preponderance of, sometimes conflicting, narratives from a variety of sources. These messages come from traditional and social media, adopting conventional and alternative formats for content and delivery. In this article, we examine the dialectical relationship between the government’s COVID-19 communication strategy and alternative messages from a select range of sources that have emerged. We evaluate the extent to which the latter messages reinforced or undermined official narratives; the relative trust that each set of messages is generating among citizens; the implications for effective management of the crisis; and steps that the state took to maintain/regain control as it sought to combat what it considered to be (dis)misinformation from some of these sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".