How the Malian press treated hydroxychloroquine at the beginning of the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background The global debate on the efficacy of hydroxychloroquine (HCQ) on COVID-19 has gone far beyond the scientific framework and has been highly politicized. These issues immediately invested the debate on HCQ and made it an object of particular crystallization. This study analyzes, through the Malian press, the echo of this debate in the national background. Methods Mixed methods design, based on a review of 452 articles about COVID-19 published by six major Malian newspapers, from January 1st to July 31st 2020. Results of a content analysis with WORDSTAT8 software were further explained by a thematic qualitative analysis using and deductive-indictive approach. Results The debate on HCQ has had very little echo in the Malian press despite some interest, because of a lack of anchoring and thus of a “response” at the national level. The national health authorities, who adopted the treatment as part of clinical trials, and the press, stayed away from both the medical and the “ideological” components of the debate, despite these a priori directly involved a country like Mali. Conclusions The paper sheds light on the issues at stake in the HCQ debate based on a case study of an atypical country in terms of impacts of Covid-19. The governance of COVID helped crystallize political opposition to the presidential regime leading to a coup in August.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".