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Record W2078887570 · doi:10.1002/psp.569

Migration trajectories of ‘highly skilled’ middling transnationals: Singaporean transmigrants in London

2009· article· en· W2078887570 on OpenAlexaff
Elaine Lynn‐Ee Ho

Bibliographic record

VenuePopulation Space and Place · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of British Columbia
FundersEconomic and Social Research Council
KeywordsEntitlement (fair division)ScholarshipImmigrationPerspective (graphical)SociologyPopulationDemographic economicsSubject (documents)EmigrationState (computer science)Migration studiesGender studiesPolitical scienceEconomicsLawDemographyComputer science

Abstract

fetched live from OpenAlex

Abstract The role played by the state in regulating population movements has been the subject of study in migration scholarship. Immigration regimes manage migration through visa restrictions stipulating the type of work migrants perform and their entitlement to rights. However, studying migration only in terms of visas or occupational categories limit a full understanding of the breadth and changing episodes making up migrant experiences. Based on a case study of ‘highly skilled’ Singaporean transmigrants in London, this paper investigates, first, the way such migrants utilise changing visa strategies to incrementally extend their stay in London. Second, the paper contextualises their migrant subjectivities and identities within the immigration and emigration regimes in which they are embedded. In so doing, the paper argues for a trajectory perspective of migration that recognises the changing strategies as well as practical and emotional challenges experienced by ‘highly skilled’ middling transnationals. Copyright © 2009 John Wiley & Sons, Ltd.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.286
Teacher spread0.273 · 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

Citations103
Published2009
Admission routes1
Has abstractyes

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