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Record W1994964546 · doi:10.1177/0096144211407743

The Impact of Immigration Policies on Transnational Filipino Immigrant Women

2011· article· en· W1994964546 on OpenAlexaboutno aff
Flávia Cristaldi, Joe T. Darden

Bibliographic record

VenueJournal of Urban History · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationResidenceCitizenshipMetropolitan areaCensusImmigration policyDemographic economicsPopulationGovernment (linguistics)GeographyPolitical sciencePublic policyEconomic growthSociologyDemographyEconomicsPolitics

Abstract

fetched live from OpenAlex

This article examines how different immigration policies and practices have influenced transnational Filipino immigrant women who chose Italy and Canada as their country of destination. The article focuses specifically on how the different immigration policies and practices have influenced the degree of incorporation of Filipino immigrant women into the social and spatial structure of the two metropolitan areas of Rome and Toronto. Data to assess the extent of incorporation were obtained from government policies and census reports. The spatial structure was analyzed by determining the level of residential segregation of the Filipino population from the majority population. The results show that one country (Canada) provides a greater avenue for social mobility, permanent residence, and citizenship than the other (Italy). Immigration policy remains one of the most important factors in understanding transnational migration.

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)0.005
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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.267
Teacher spread0.233 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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