Bibliographic record
Abstract
This article studies one of the humanitarian challenges caused by the COVID-19 crisis: the dignified handling of the mortal remains of individuals that have died from COVID-19 in Muslim contexts. It illustrates the discussion with examples from Sunni Muslim-majority states when relevant, such as Egypt, the Kingdom of Saudi Arabia, Jordan, Morocco and Pakistan, and examples from English-speaking non-Muslim majority states such as the United Kingdom, the United States of America, Canada and Australia as well as Sri Lanka. The article finds that the case of the management of dead bodies of people who have died from COVID-19 has shown that the creativity and flexibility enshrined in the Islamic law-making logic and methodology, on the one hand, and the cooperation between Muslim jurists and specialised medical and forensic experts, on the other, have contributed to saving people’s lives and mitigating the effects of the COVID-19 pandemic in Muslim contexts.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".