Systematic conservation planning within a Fijian customary governance context
Bibliographic record
Abstract
Although conservation planning research has influenced conservation actions globally in the last two decades, successful implementation of systematic conservation plans in regions where customary marine tenure exists has been minimal. In such regions, local community knowledge and understanding of socioeconomic realities may offer the best spatially explicit information for analysis, since required socioeconomic data are not available at scales relevant to conservation planning. Here we describe the process undertaken by the Kadavu Yaubula Management Support Team, a team of researchers from The University of the South Pacific and the local communities to assess whether systematic conservation planning tools can be effectively applied and useful in a customary governance context, using a case study from Fiji. Through a participatory approach and with the aim of meeting local-scale conservation and fisheries needs, a spatial conservation planning tool, Marxan with Zones, was used to reconfigure a collection of locally designed marine protected areas in the province of Kadavu in order to achieve broader objectives. At the local scale, the real value of such tools has been in the process of identifying and conceptualising management issues, working with communities to collate data through participatory techniques, and in engaging communities in management decision making. The output and use of the tool has been of secondary value. The outcome was invaluable for developing marine protected area network design approaches that combine traditional knowledge with ecological features in a manner appropriate to a Melanesian context.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".