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Record W4281752911 · doi:10.1177/00420980221087048

Moving nurses to cities: On how migration industries feed into glocal urban assemblages in the care sector

2022· article· en· W4281752911 on OpenAlexafffundabout
Félicitas Hillmann, Margaret Walton‐Roberts, Brenda S. A. Yeoh

Bibliographic record

VenueUrban Studies · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGlocalizationEconomic geographyBusinessUrbanizationEconomic growthRegional scienceSociologyGeographyGlobalizationEconomicsMarket economy

Abstract

fetched live from OpenAlex

Migration industries include a diverse array of migration-related services provided by the state, commercial agents, humanitarian organisations and migrant social networks. The work performed by this array of providers, both non-state and state actors, includes facilitating, filtering/channelling and constraining migration. As a powerful example of how migration industries work in general, we examine their dynamics in the care sector as part of glocal (care) chains involved in the migration of nurses. The article provides a conceptualisation of the role of the 'migration industry' as part of a changing global business in the field of care work. We direct our attention to the drivers and institutions that facilitate and shape the arrangements of international care mobility and the constitution of glocal urban assemblages. Drawing on three models of nurse migration - bus stop (Philippines-Singapore), two-step (India-Canada) and triple-win (Vietnam-Germany) - we show how the socio-spatial configurations of glocal urban assemblages linked to the three models yield different social integration outcomes for migrant nurses.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.418
Teacher spread0.340 · 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.

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

Citations12
Published2022
Admission routes3
Has abstractyes

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