Conservation Narratives and Their Implications in the Coral Triangle Initiative
Bibliographic record
Abstract
Conserving coral reefs, sustaining fisheries, and ensuring food security are multi-faceted challenges. Six nations in the Southeast Asia Coral Triangle have agreed to a region-wide framework to address these challenges through the Coral Triangle Initiative on Coral Reefs, Fisheries and Food Security (CTI). Based on a review of documentation, selected discussions and ongoing work in the region, we offer an initial assessment of narratives influencing conservation practice in the CTI. Current efforts in the CTI are framed by a crisis narrative that emphasises the importance of maintaining critical ecosystems and baseline conditions. This narrative has a strong empirical basis but it can also exacerbate a dualistic view of people and nature. However, CTI documentation and programming also reflect a recognition of linked social-ecological change and the historical co-evolution of communities and coastal-marine systems. This emerging narrative places an emphasis on building resilience to change, rather than resisting change. We do not advocate here for a single narrative with which to frame policy responses in the CTI, but rather draw attention to the ways that mainstreaming of certain narratives will have material effects on initiatives and programmes promoted in this region of globally significant marine biodiversity.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".