Restorative practices, marine ecotourism, and restoration economies: revitalizing the environmental agenda?
Bibliographic record
Abstract
In this study, we introduce the concept of restorative marine ecotourism (RME) to explore the potential environmental gains of coupling marine ecotourism operations and marine restoration initiatives. Restoring marine ecosystems has become a priority in the international environmental agenda and the field needs novel management strategies to overcome the main challenges. Marine ecotourism provides an opportunity to couple business-based activities and ecological restoration in marine habitats in ways that produce benefits for both marine habitats and local communities. Currently, examples of good practice in restorative economy are rare, but by highlighting solution-focused objectives and practical applications we identify opportunities to realize these benefits through RME. We pay particular attention to the social-ecological factors that might drive RME initiatives in specific sites. We derive insights from land restoration practices and governance, and from existing literature on both marine ecotourism and marine ecological restoration. Focusing on diving-based tourism, we propose a set of starting points for implementing RME. We identify the potential presented by cross-sector collaborations, restorative investments, and citizen science as platforms for developing RME protocols and encouraging RME initiatives.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".