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Record W2237616429 · doi:10.4236/ojms.2016.61006

Validating Governance Performance Indicators for Integrated Coastal and Ocean Management in the Southeast Region of Cuba

2016· article· en· W2237616429 on OpenAlexaff
Fajardo José Abelardo Planas, Lucia Fanning, Camilo M. Botero

Bibliographic record

VenueOpen Journal of Marine Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEnvironmental resource managementSustainabilityCoastal managementEnvironmental planningCorporate governanceMarine spatial planningGeographyIntegrated coastal zone managementCoastal zoneBusinessEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The importance of evaluating the success of policies developed to effectively manage coastal and marine resource use is well documented. However, few frameworks exist that allow for a comparative examination of existing policy instruments, as opposed to specific initiatives, which assess governance performance aimed at addressing issues arising in the coastal zone. This paper describes the process and findings for evaluating the feasibility of a modified Coastal Sustainability Standard (CoSS) framework that seeks to measure the effectiveness of individual planning instruments within overlapping spatial boundaries in the landward and marine areas in the southeast region of Cuba. Through workshops conducted in Santiago de Cuba and Guamá municipalities with key representatives involved in coastal management and planning, the utility of the framework was assessed using the main instruments of territorial planning in Cuba, namely integrated river basin management, territorial planning in coastal municipalities and marine and coastal management. While, the findings suggest that the modified CoSS framework can be used to assess the effectiveness of these planning instruments in the region, and workshop participants also suggested improvements to better match its use to the characteristics of the study region.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.006
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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 designObservational
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

Citations15
Published2016
Admission routes1
Has abstractyes

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