Including local voices in marine debris conversations to advance environmental justice for island and coastal communities: perspectives from St. Paul Island, Alaska
Bibliographic record
Abstract
Marine debris is ubiquitous across the global ocean and is an increasing threat to human health, economies, habitats, and wildlife. While local to national action plans are important in addressing this issue, they do not necessarily reflect the needs of coastal communities most heavily impacted. Remote island and coastal communities, particularly in Alaska, do not generate the majority of marine debris impacting their ecosystems; however, they are often left with the task of removal and disposal. Thus, the detrimental effects of marine debris are not only an ecological problem but an issue of environmental justice. This project aimed to catalyze the inclusion of place-based knowledge in marine debris solutions for St. Paul Island, a predominantly (>85%) Alaska Native community in the Bering Sea. We interviewed 36 community members during 2017–2020, documenting their observations of marine debris types, amount, distribution, and impacts over recent decades. Participants reported increasing plastic debris since the 1980s, particularly plastic bottles and fishing gear. Nearly 80% expressed concern about impacts to subsistence resources, including entanglement and ingestion. St. Paul Island community members’ experiences highlight that solving marine debris issues requires broader policies and mitigation strategies addressing sources of debris and advancing environmental justice by impact reduction. Furthermore, this case study can serve as an example of how locally relevant action plans can be developed in other coastal communities around the world by including knowledge and concerns of community members, as they are the most heavily and personally impacted by the marine debris on their shorelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".