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Record W2009044735 · doi:10.3390/su7056046

Notes on the Quality of Life of Artisanal Small-Scale Fishermen along the Pacific Coast of Jalisco, México

2015· article· en· W2009044735 on OpenAlexafffund
Myrna Leticia Bravo Olivas, Rosa María Chávez Dagóstino, Christopher D. Malcolm, Rodrigo Espinoza-Sánchez

Bibliographic record

VenueSustainability · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsBrandon University
FundersConsejo Nacional de Ciencia y TecnologíaYork UniversityUniversidad de Guadalajara
KeywordsScale (ratio)GeographyQuality (philosophy)FisheryBusinessCartographyBiology

Abstract

fetched live from OpenAlex

Sustainable fishing includes the socioeconomic status of fishers. We combined empirical quality of life (QOL) and subjective lived experiences methods to explore the social sustainability of artisanal fishers in five fishery collectives along the coast of Jalisco, Mexico, where the average daily income is slightly above the poverty level. The QOL scores were also related to annual catch and incomes within each collective. A QOL index is used in this study that combines importance and achievement ratings scores; the results are indicative of an acceptable QOL for fishermen. The concept of lived experiences, incorporating aspects of life relating to Mind, Body, Work and People was explored through interviews with 12 fishers. The QOL data revealed that family and friends are important indicators related to positive QOL reported by the sample, while economic indicators were not important. Although four of the five collectives perceived that the future looks worse than the present and past, there was limited correlation between catch or income and QOL. However, while the lived experiences exercise in part supported the QOL findings, in that People was the most important dimension for almost all of the fishers interviewed, negative economic gaps related to poor catches and incomes were prevalent in the Mind and Work dimensions. The findings suggest that to understand the socioeconomic component of sustainable fisheries, both of these approaches should be considered, as they can illuminate different aspects of fishers’ lives that need to be considered during the development of fisheries’ management policies.

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)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.323
Teacher spread0.271 · 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

Citations13
Published2015
Admission routes2
Has abstractyes

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