Coastal erosion narratives in the Gulf of Mexico: implications for climate change governance
Bibliographic record
Abstract
This article presents a study of coastal erosion narratives by the Mexican government, scientists, and local fishers in coastal communities in the Gulf of Mexico. It shows how plans to enroll fishing communities into programs to adapt to or to slow coastal erosion are based on simplified environmental narratives that rely on global climate change as the main cause of coastal erosion. They discount local processes and local explanations, as well as scientific studies that outline complex multi-scalar explanations for coastal erosion. Government narratives frame global climate change, as manifested in increased frequency and intensity of hurricanes and other hydrometereological extreme events and sea level rise, as the main causes of changes in coastal environments, including coastal erosion They fail to acknowledge other causes including the environmental degradation caused by the influential oil industry. In contrast, fishers' more complex and locally-embedded narratives are shaped by their long-term struggles against the state-owned oil company, whom they hold primarily responsible for coastal erosion in their communities. Scientists similarly emphasize the importance of local and regional processes, with climate change understood primarily as having significant impacts in the future, but less so in the recent past. Differences in temporal and geographical scaling among these narratives highlight the importance of considering how the translation of climate change adaptation programming from the global to diverse local situations would ideally consider site-specific power relations as well as community-based perspectives.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".