Land-sea interactions and coastal development: An evolutionary governance perspective
Bibliographic record
Abstract
Coasts are changing at an impressive speed. Therewith come changes in and challenges to governance that require an empirically-based understanding in order to foster sustainability transitions. New challenges are often not adequately met, so a host of problems arise. The papers in this special issue speak to these problems and consider which governance approaches might be worth exploring. The authors look at a diverse set of governance practices and changes, using the lens of Evolutionary Governance Theory (EGT). This theoretical approach is chosen, because EGT offers a perspective on governance which gives central place to co-evolution. EGT integrates a broad range of theoretical notions, drawing on evolutionary and system theories, institutional economics and versions of post-structuralism. EGT is put to use to analyse what is called in the framing paper ‘the coastal condition’. It is argued that governing land-sea interactions and the coastal zones is particularly prone to problems of observation (between land and sea, between centre and coastal margin) and complex interdependencies (between social and ecological systems, between actors managing risk). Governing land-sea interactions requires multi-level governance and new forms of policy integration, which means, an explicitly coastal governance arena, semi-autonomous yet subjected to the checks and balances of a multi-level system. The various papers develop these insights by highlighting problems of coordination in coastal governance, issues of inclusion/exclusion, diverse knowledges and observations. They illustrate how the coastal condition engenders risk and uncertainty, and how it renders policy integration more important, while simultaneously making it harder to achieve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".