“When the ground opened”: Responsibility for harms and rights violations in disasters – Insights from Sierra Leone
Bibliographic record
Abstract
So-called “natural” disasters are often characterized by major human rights abuses, yet responsibility and accountability for such violations have attracted relatively limited attention in research and practice. Instead, these events and survivors’ suffering are often dismissed as “acts of God” or tragic misfortunes. Through analysis of an under-examined disaster—the 2017 mudslide in Freetown, Sierra Leone—this article probes survivors’ perspectives on responsibility for disasters, and suffering and violations accompanying them. While survivors in this case often attribute responsibility to God or other supernatural forces, they also understand the state and other earthly actors as sharing different forms and degrees of responsibility for the disaster and its harmful consequences. Indeed, seeing the mudslide as an “act of God” does not absolve the state from its obligation to protect citizens from harms associated with disasters and subsequent response efforts. Survivors’ perspectives provide significant insight into the challenge of advancing accountability in disaster 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 | 0.003 | 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 | 0.009 | 0.019 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".