Governance of ecosystem services on small islands: three contrasting cases for St. Eustatius in the Dutch Caribbean
Bibliographic record
Abstract
Natural ecosystems provide an attractive focus for tourism on small islands. However, at the same time tourism and other human actions can be detrimental to these ecosystems especially because governance of the ecosystem may be difficult due to the limited resilience of small island ecosystems. In this paper, we focus on the conditions under which self-governance will be the appropriate governance mechanism of ecosystem services on small islands. We apply Ostrom’s (2009) framework for common-pool resources in a socialecological system, and select the relevant indicators for small islands. We scored these indicators for three cases (environmental issues) in St. Eustatius, a Caribbean island under Dutch rule. These cases show that self-organization of ecosystem services is not an outcome easily achieved. The unevenly distributed benefits of potential measures are found to decrease community support of measures that could reinforce these ecosystem services.
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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 | 0.001 | 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".