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Record W2899886762 · doi:10.1080/1369183x.2018.1544487

Intergenerational strategies: the successes and failures of a Northern Thai family's approach to international labour migration

2018· article· en· W2899886762 on OpenAlexaff
Sarah Turner, Jean Michaud

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversité LavalMcGill University
Fundersnot available
KeywordsTransnationalismLivelihoodEmic and eticAgency (philosophy)Migration studiesSociologyWork (physics)Focus groupKinshipGender studiesEconomic growthPolitical scienceGeographySocial scienceEconomics

Abstract

fetched live from OpenAlex

International work migration from rural Thailand is not new, yet relatively little is known about the decision-making processes regarding this livelihood strategy at the family level and across generations. Drawing on concepts of transnationalism and livelihood pathways and trajectories, this case-study traces the agency that underpins labour moves over two generations of a rural family in Chiang Rai province. The focus is on individual trajectories that exemplify how the first generation of migrant labour entered the market and the degree to which the second generation is replicating or modifying the migration patterns of their elders. We also show, from an emic perspective, who is deemed to be the most and least successful in their livelihood approach. To do so, we draw on data gathered from life stories, conversational interviews, and village visits, focusing on 45 individuals and spanning a 30 year timespan of international work migration. Moves to Saudi Arabia, Hong Kong, Japan, South Korea, Singapore, Australia and a failed endeavour to reach New Zealand are analysed, in an attempt to contribute to debates on transnationalism while highlighting individual and generational differences in migration stories, the specific roles of brokers and informal social networks, and diverse spatial practices.

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 score0.253
Threshold uncertainty score0.898

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 science0.0000.000
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 head0.054
GPT teacher head0.370
Teacher spread0.316 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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