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Record W4389318844 · doi:10.5751/es-14273-280425

Environmental change and resource access in aquatic food systems: a Photovoice case study of Cambodian fisheries

2023· article· en· W4389318844 on OpenAlexvenueno aff
Kathryn J. Fiorella, Heather Magnuson, Antara Finney Stable, Chork Sim, Voleak Phan, Elizabeth G. Fox

Bibliographic record

VenueEcology and Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
FundersNational Geographic Society
KeywordsPhotovoiceLivelihoodEcosystem servicesFishingEnvironmental resource managementResource (disambiguation)SustainabilityBusinessGeographyFisheryEnvironmental planningEcologyEcosystemEconomic growthAgricultureEconomics

Abstract

fetched live from OpenAlex

Ecosystem services and the biodiversity that supports them directly provision food and livelihoods to millions around the world within environments increasingly facing multifaceted changes. Yet the perspectives of resource users on the value of those resources and the challenges they face amid social-ecological change are still too often poorly understood. In this study, we use Photovoice methodology and a social-ecological systems perspective to understand the value of access to fish resources and the impacts of changing access for small-scale fishing communities in Cambodia. Contrasting the perspectives of households in different ecological settings, including adjacent to the Tonle Sap Lake and within its floodplain, revealed stark differences in the experiences of regulation enforcement and fisheries management for communities that had viable alternatives to fishing compared to those without options beyond fishing. The study addresses the need to understand both the lived experiences of those on the frontlines of environmental changes, and to disentangle the heterogeneous experiences across and within communities to improve resource management and community support in complex, changing social-ecological systems.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.064
GPT teacher head0.289
Teacher spread0.225 · 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 designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

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