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Record W3107373078 · doi:10.1111/glob.12308

Infrastructure of mobility: navigating borders, cities and markets

2020· article· en· W3107373078 on OpenAlexaff
Maria Cecilia Hwang

Bibliographic record

VenueGlobal Networks · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsMcGill University
Fundersnot available
KeywordsCirculation (fluid dynamics)DestinationsImmigrationMultinational corporationGlobal cityEconomic geographySettlement (finance)GeographyState (computer science)EconomyPolitical scienceBusinessEconomicsTourismEngineeringFinance

Abstract

fetched live from OpenAlex

Abstract In this article, which is based on research conducted in Hong Kong from 2010 to 2013, and again in 2018, I analyse the region‐bound circulation of independent women sex workers from the Philippines across the Asian global cities of Hong Kong, Singapore, Kuala Lumpur, and Macau. Migrating as visa‐free tourists, they maintain a valid immigration status and maximize their income in the informal economy by adopting a step‐down transient mobility pattern characterized by a downward hierarchical circulation to multiple city‐states and country destinations. As I show, their multinational migration is produced and structured through the instrumentalization of a mobility infrastructure constituted by a regime of visa‐free circulation, transportation developments, commercialized migration services, and social networks. In this article, I attempt to extend the contemporary analysis of unauthorized migration by moving away from the prevailing focus on incorporation and settlement and towards an examination of the logics and mechanics of migration flows that occur outside state‐sanctioned channels.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.008
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.282
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

Citations6
Published2020
Admission routes1
Has abstractyes

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