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Record W4413993446 · doi:10.17645/oas.10338

Strategies for Transforming Coastal Governance: Addressing Interdependent Dimensions

2025· article· en· W4413993446 on OpenAlexaff
Mafaziya Nijamdeen, Ansje Löhr, Kristof Van Assche, Raoul Beunen

Bibliographic record

VenueOcean and Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Alberta
FundersEuropean CommissionUK Research and InnovationHORIZON EUROPE Framework ProgrammeGovernment of the United Kingdom
KeywordsInterdependenceCorporate governanceProcess managementEnvironmental planningBusinessEconomic geographyPolitical scienceEnvironmental resource managementRegional scienceGeographyEnvironmental scienceFinance

Abstract

fetched live from OpenAlex

Coastal areas are places where land and sea meet. These places offer many socio‐economic opportunities but also face profound social and environmental challenges that are often exacerbated by limitations in current governance systems. These limitations include a lack of coordination, unclear mandates and roles, fragmented knowledge, power dynamics, and insufficient stakeholder involvement. Transforming coastal governance is therefore needed to enhance the effectiveness and legitimacy of governance systems and their institutions, but current practices and past experiences have shown that changing governance is anything but easy. In this article, we analyse how three critical governance dimensions: (1) forms of integration of land and sea management; (2) forms of knowledge mobilized; and (3) forms of democracy in their interplay, shape possibilities and limits for transforming governance. Drawing on insights from the literature and three case studies from Spain, the UK, and Norway, we highlight how these different governance dimensions are strongly interrelated and should be addressed in coherent ways to make governance more effective and legitimate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.246
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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