Governance innovations in the coastal zone: Towards social-ecological resilience
Bibliographic record
Abstract
Innovation is championed to enable transformation towards social-ecological resilience in coastal communities. Yet, innovation in coastal areas is not well understood with limited research concerning the nature of innovations and determinants of success. Analysis of interviews with 68 coastal and community key informants in Australia’s most rapidly growing coastal communities revealed that despite high levels of individual capacity (e.g., among coastal managers and community service providers) and good-practice policy, most innovations were limited in scale and insufficient for transformative change. All too familiar barriers included limited financial and human capacity, and a culture of ‘failure avoidance’ in government. Nevertheless, a small number of exemplars avoided these constraints by implementing systemic solutions that addressed socio-ecological challenges and built community resilience. Individual and community capacity for such innovation was built prior to crisis events and consisted of experience/knowledge, extensive and diverse social networks, and resource mobilisation skills. The findings provide further evidence of the critical importance of investing in communities before, during, and following crisis—in other words, continually.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| 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".