‘Subaltern’ pushbacks: An analysis of responses by Facebook users to ‘racist’ statements by two French doctors on testing a COVID-19 vaccine in Africa
Bibliographic record
Abstract
In April 2020, two French doctors discussed on television the idea of testing a COVID-19 vaccine in Africa. The controversial utterances were widely condemned, subsequently leading the doctors apologizing. Using thematic analysis, and drawing on Stuart Hall’s encoding–decoding model and the concepts of coloniality and decoloniality, this article analyses responses to the doctors’ statements by social media users. Of the decoding positions proposed by Stuart Hall, many Facebook users occupied the oppositional decoding position. Facebook users dethroned ideas rooted in colonialism that positioned Europeans as superior thought leaders and Africans as inferior and passive recipients of western knowledges and leadership. They also dismissed the doctors as flagrant racists. Facebook users affirmed that Africans were not guinea pigs and Africa was not a laboratory. The visceral pushbacks by social media users discredited and delegitimized the doctors’ ideas as well as to foster solidarity among Africans in disparate locations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".