Fears, Deaths, Mourning, and Burials in Times of COVID-19 Pandemic in Nigeria
Bibliographic record
Abstract
Extant studies on deaths in Nigeria have been conducted without much critical focus on how the public responded to emergency politics or the government’s interventions during pandemics. Fundamentally, this is the gap this research aims to fill. This study focuses on pain events, grieves, and mourning following COVID-19-related deaths. Thus, this study analyzes the intersections between COVID-19 and death discourse by exposing and interrogating the variances, ambiguities, ambivalences, corollaries, and paradoxes amid convoluted public health conversations. This research employed both primary and secondary sources. Primary sources include African beliefs, newspapers reports of past and current pandemics, and radio, television, and social media narratives. Secondary sources include reviews of existing literature on deaths and pandemics. Historical analysis is used in this study, identifying two categories of dead bodies created during the COVID-19 pandemic. The first category is Pandemic Dead Bodies (PDBs) and the second category is Non-Pandemic Dead Bodies (NPDBs). Many concomitants characterizing Pandemic Dead Bodies including stigmatization, apathy, otherization, ambiguities, genderization, demographication, politicization, contestations, and weaponization are interrogated from socio-historical perspectives. Heightening the stress of grieving families are issues around deaths, as burials are postponed or held within the restrictions of National Centre for Disease Control (NCDC) COVID-19 protocols, often with the presence of limited family members of the deceased. Thus, the pains and grieves are not just about the loss of loved ones, but the inability to give them a befitting burial, since Nigerians love to celebrate the liminality of their loved ones into eternity. Likewise, anticipatory grief became more accentuated and aggravated in Nigeria regarding the manner of announcing the demise of some popular politicians who died of COVID-19. Fundamentally, these problematic encumbrances, nuances, and intrigues concerning deaths during the COVID-19 pandemic are historicized. This study concludes that pains, grief, sorrow, death, and burial are historically constituted and configured regarding social, economic, political, cultural, and environmental interactions.
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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 | 0.002 | 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 | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".