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Record W2982520863 · doi:10.1111/sjtg.12300

Exploring livelihood transitions in the Mekong delta

2019· article· en· W2982520863 on OpenAlexaff
Gordon Betcherman, Iftekharul Haque, Melissa Marschke

Bibliographic record

VenueSingapore Journal of Tropical Geography · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsUniversity of OttawaInternational Development Research Centre
Fundersnot available
KeywordsLivelihoodAgriculturePovertyDeltaGeographyFishingBoomBusinessSocioeconomicsNatural resource economicsEconomic growthFisheryEconomics

Abstract

fetched live from OpenAlex

Important environmental and economic changes are transforming livelihoods in coastal communes throughout the Mekong delta. In the process, the historical reliance on rice farming and fishing has become less viable and sustainable, forcing households to construct more complex livelihood strategies. To document these livelihood transformations, we have analyzed primary survey data from 346 households across eight coastal communes in Ca Mau Province, located in the southern tip of Vietnam's Mekong delta. We find that there was a great deal of change in the livelihoods of survey respondents over the decade covered by the survey with over 40 per cent of all households reporting a change in their primary source of income. For the most part, these changing livelihood patterns describe a successful adjustment process whereby many households partly or completely left activities that did not offer high returns for other activities that offer more, or were able to combine livelihood activities to successfully adjust. However, we argue that not all fisheries‐based households have the financial or human capital to take advantage of the opportunities arising from Vietnam's seafood boom or economic development more generally. Poverty remains prevalent for those locked into extensive aquaculture and especially farming.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.276
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2019
Admission routes1
Has abstractyes

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