A comparative case study of multistakeholder responses following oil spills in Pointe d’Esny, Mauritius, and Huntington Beach, California
Bibliographic record
Abstract
Oil spills generate negative ecological, societal, economic, and public health impacts, and require rapid response to contain and mitigate damages. Prompt and effective emergency management of acute events like oil spills is highly dependent on the social, institutional, and ecological context. In August 2020, the wreckage of the MV Wakashio spilled 1000 tonnes of fuel oil along an ecologically sensitive coastline in Pointe d’Esny, Mauritius. In October 2021, an offshore pipeline split and released 78 tonnes of crude oil off the coast of Huntington Beach in California. We compare responses among three sets of stakeholders (government, non-governmental organizations, and local residents) during the first 10 days of both oil spills, which also occurred during the COVID-19 pandemic. In Mauritius, unfavorable weather conditions and COVID-19-related border closures that delayed international support impeded government action, creating a leadership and trust vacuum among residents regarding the immediate cleanup response. This perceived gap was subsequently complemented by NGOs coordinating improvised artisanal boom production and local volunteer cleanup efforts, with limited protection or public health training. By contrast, prompt state and local government intervention in Huntington Beach created a clear chain of command with NGOs and residents deferring to official guidance. In both cases, the oil spills created new policy opportunities to improve emergency management plans and reduce future risks. Our results demonstrate the influence of prior local expertise in managing earlier disasters and resources on governmental and organizational capacity. Incorporating and ensuring on-the-ground disaster expertise in response activities improves government-led crisis response, subsequently protecting ecosystems and residents. Effective multi-level crisis response helps address a range of environmental and social justice concerns related to negative impacts of spills on local communities. Our study discusses how learnings from disaster management can reinforce social-ecological resilience in coastal communities dealing with increasing anthropogenic stressors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".