Adaptive governance and ecological restoration: lessons from three Australian regulatory frameworks
Bibliographic record
Abstract
Abstract Introduction Ecological restoration has gained significant international traction as a response to biodiversity loss and climate change across the globe. Regulation can be a key factor in facilitating ecological restoration and the successful recovery of ecosystems; however, regulation for ecological restoration is still emerging at both national and international levels. Objectives This paper examines the adoption of adaptive governance in three case studies of ecological restoration regulation from Australia to investigate the adoption of adaptive governance within regulatory frameworks and what can be done to improve adaptive governance adoption in ecological restoration regulation. Methods Data from in‐depth interviews with 34 ecological restoration experts from across three Australian jurisdictions forms the basis of a comparative case study that compares adaptive governance characteristics across three case studies of ecological restoration regulation. Results This research found that adaptive governance is minimally reflected in the case studies and makes recommendations for its further incorporation in the regulatory frameworks. Conclusions This research suggests that the adoption of adaptive governance is important but so too is ensuring strong regulatory standards for ecosystem recovery, as well as appropriate funding, monitoring, and enforcement where relevant.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".