Seaweed blooms in paradise: Ecological reflexivity, governance and the Sargassum crisis in the Mexican Caribbean
Bibliographic record
Abstract
Seaweed blooms pose a compelling governance challenge caused by the new environments of the Anthropocene. Along the Quintana Roo coastline, nestled in the heart of the Caribbean, the onset of extensive Sargassum infestations began in late 2014, posing a formidable environmental management dilemma for state and federal authorities. This study describes the institutional responses elicited by the Sargassum influx on Mexico’s Caribbean shoreline, particularly focusing on Cancún and the Riviera Maya. It proposes ecological reflexivity as a promising governance principle for institutions faced with increasingly complex and unforeseeable circumstances, such as the massive arrivals of Sargassum. Based on a comprehensive analysis of national press reports, active participation in forums and seminars, and in-depth interviews, our research identifies three distinct governance phases. We explore these phases considering the concept of ecological reflexivity. Our findings make a strong case for acknowledging institutional errors and shortcomings as an indispensable aspect of formulating effective strategies to combat unexpected and unfamiliar phenomena such as seaweed blooms. Moreover, governance strategies for dealing with Sargassum in Quintana Roo should not only consider responding to human interests and sustaining the tourist industry. Instead, they should encompass an approach that considers the interplay between human and non-human components within the socio-ecological system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".