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Record W4402713399 · doi:10.1007/s13280-024-02062-z

Small-scale fisheries in ecologically sensitive areas in Latin America and the Caribbean: Do marine protected areas benefit fisheries governance?

2024· review· en· W4402713399 on OpenAlexaff
Ana Cinti, L. M. Ramírez, Mauricio Castrejón, Jaime A. Aburto, Luciana Loto, Stuart Fulton, Mario Rueda, Alexandre Schiavetti, Francisco J. Fernández‐Rivera Melo, Manuel Alvarez Bravo, Daniela Trigueirinho Alarcon, Valéria Penchel Araújo, Ana M. Parma

Bibliographic record

VenueAMBIO · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsFisheries and Oceans Canada
FundersFondo para la Investigación Científica y TecnológicaRufford Foundation
KeywordsFisheryLatin AmericansMarine protected areaCorporate governanceCaribbean regionFisheries scienceScale (ratio)Marine reserveGeographyFisheries managementMarine fisheriesBusinessEcologyFishingPolitical scienceBiologyFinanceCartography

Abstract

fetched live from OpenAlex

Many small-scale fisheries (SSFs) in Latin America and the Caribbean (LAC) operate in ecologically sensitive areas, where balancing conservation and resource use is challenging. 'Multiple-use' marine protected areas (MPAs) have been implemented to accommodate SSFs (among other uses) within areas designated for conservation, creating opportunities and challenges for SSF governance. We analyzed eleven case studies from LAC to explore: (1) how different MPA institutional designs affect key aspects of SSF governance and (2) the links between these effects and the type of initiative that promoted MPA establishment (origin). Results indicate that the existence of an MPA benefited SSF governance in many ways, with more pronounced positive effects in MPAs with mixed to bottom-up origin supported by well-organized fishing groups. In addition, the presence of supportive MPA authorities that leveraged local capacities and initiatives and adopted flexible and collaborative governance systems made a difference in several cases. Lessons for integrating MPA and SSF governance are drawn.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2024
Admission routes1
Has abstractyes

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