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Record W4323663893 · doi:10.1080/09669582.2023.2186826

Asset assemblages and livelihood resilience in a coastal community

2023· article· en· W4323663893 on OpenAlexaff
Chao Wei, Honggang Xu, Geoffrey Wall

Bibliographic record

VenueJournal of Sustainable Tourism · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLivelihoodAsset (computer security)Psychological resilienceSustainabilityFinancial capitalNatural resource economicsTourismCapital assetBusinessResilience (materials science)Environmental resource managementGeographyEconomicsHuman capitalEconomic growthFinanceEcologyAgriculture

Abstract

fetched live from OpenAlex

Both livelihood resilience and sustainability have received substantial attention from tourism researchers. Capital is crucial to livelihood resilience, and different livelihoods depend on different asset portfolios. However, the transformation of capital across livelihoods has not been examined carefully. Instead of treating capital as singular, we investigate capital as a multiplicity. We evaluate the transformation of capital through a qualitative approach to better understand livelihood resilience through changes in asset assemblages. We made multiple field visits to a Chinese coastal community that is developing a tourism industry, where we collected data through semi-structured interviews and observation that revealed that livelihood capitals have multiple properties and functions, enabling their transformation among asset assemblages. The buffering capacity of livelihood systems, and thereby the livelihood resilience of fishing households, substantially depends on the transformation of capitals. We conclude that the understanding of livelihood resilience can be deepened through assemblage thinking that considers both the multiplicity of capitals and their relations in different production contexts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.448

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.227 · 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
Published2023
Admission routes1
Has abstractyes

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