Mistrust in Dysfunction, Culture in/Sensitivity in Era of Pandemics: How Ibibio People of South Eastern Nigeria Responded to 1918-1921 Influenza Vis a Vis COVID-19 Pandemics
Bibliographic record
Abstract
In an exhibition of social responsibility against COVID-19 outbreak, almost all governments of African nations have taken proactive measures to close down potential sources of the virus transmission to the public. In this paper we engaged sequential text interpretation of historical and current health documents, professional publications and media reportage on the post WW1 influenza and the ongoing COVID-19 pandemics in order to fully explore the tensions and issues arising from the responses of Indigenous Ibibio people of South Eastern Nigeria to the pandemics’ interventions. We employed anti-colonial theory and the philosophical trajectory of African traditional ecological knowledge, TEK to investigate the two devastating global pandemics. We observed that the Ibibio people’s responses to intervention efforts of the ruling authorities and NGOs in the two pandemics were shaped by suspicion, and as the world is urgently turning into a densely populated global village, trust has become an essential commodity with respect to ways professional multinational corporations and agencies deal with the Indigenous peoples around the world. Non-toxic, unbiased, and accurate information from media reportage is sine qua none in fostering trust and restoring the confidence of Indigenous communities in Western science, a condition marred by misinformation and experiences of racism and stereotype. We argued that a multi-world approach to understanding and finding solutions to global socio-cultural, and health problems will provide sustained benefit to the multicultural society the world has become. We conclude by advising that the continents of the world share a collection of diverse Indigenous peoples’ epistemic saliencies (lived experiences). The unique ecological approach each of the Indigenous communities has lived with over the centuries constitutes their identity with the ecological environment. To gain the trust and acquiescence of these communities with respect to disaster interventions, these beliefs and practices, including in matters of health emergency, should be, evaluated, and incorporated into programs intended for the Native peoples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".