New Regionalism and Planning for Water Quality Improvement in the Great Barrier Reef, Australia
Bibliographic record
Abstract
Abstract New regionalism encompasses a diversity of approaches to address regional planning problems. Within Australia, the Great Barrier Reef Water Quality Protection Plan was developed to enhance water quality within the World Heritage‐listed Great Barrier Reef, and the plan gave responsibility to regional, natural resource management bodies to undertake several actions. This paper evaluates these initiatives in the light of the emerging theory of new regionalism and highlights six main lessons: up‐scaling of the catchment approach to a reef‐wide approach is essential in order to improve water quality, but must be complemented by cross‐regional collaboration; new governance and institutional arrangements and strengthened partnerships must be effectively integrated; culture and history are important in determining the most effective management approaches; pilot projects must move to comprehensive and strategic implementation; science is important but needs to incorporate other branches of knowledge; and economic incentives are important in encouraging the implementation of best practices, but delivery needs to be flexible. We conclude that the new regional approach is appropriate for addressing complex, multi‐scale problems such as water quality, and has incorporated several key principles of new regionalism, but that the process must move quickly to a higher level of commitment and application.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".