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Record W2099021621 · doi:10.1186/2212-9790-11-3

Adaptive learning, technological innovation and livelihood diversification: the adoption of pound nets in Rio de Janeiro State, Brazil

2012· article· en· W2099021621 on OpenAlexafffund
C. Julián Idrobo, Iain J. Davidson‐Hunt

Bibliographic record

VenueMAST. Maritime studies/Maritime studies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research CentreUniversity of Manitoba
KeywordsDiversification (marketing strategy)LivelihoodRestructuringFishingPound (networking)NarrativeEconomyBusinessEnvironmental resource managementKnowledge managementEconomicsGeographyPolitical scienceMarketingComputer scienceAgricultureFinance

Abstract

fetched live from OpenAlex

This paper examines the adoption of a technology to appropriate an ecologically constrained resource within the context of a restructuring fisheries sector utilising the conceptual lenses of adaptive learning and practice. Participant observation and semi-structured interviews were undertaken in the coastal community of Ponta Negra, Paraty, Rio de Janeiro, Brazil, from May 2010 to March 2011. The materials collected were translated and transcribed into English and then manually coded. Through a restorying process the English transcripts were developed into an analytical narrative that described the process of the adoption of pound nets and how this initiated a process of social differentiation between fishing households. The pound net technology constituted a new field of practice that both created and constrained opportunities for livelihood diversification. In this case, individual adaptations made to diversify household economies initiated a cascading process of social differentiation within a coastal community.

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)0.001
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 score0.171
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.272
Teacher spread0.230 · 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

Citations10
Published2012
Admission routes2
Has abstractyes

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