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Record W2082594473 · doi:10.1002/psp.301

Geography and segmented assimilation: examples from the New York Chinese

2004· article· en· W2082594473 on OpenAlex
K. Bruce Newbold, Matthew Foulkes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePopulation Space and Place · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsMcMaster University
FundersNational Science Foundation
KeywordsNaturalizationImmigrationAcculturationGeographyAssimilation (phonology)Socioeconomic statusMetropolitan areaEconomic geographyDemographySociologyCensusPopulationLinguistics

Abstract

fetched live from OpenAlex

Abstract Drawing upon the segmented assimilation framework, and using the 1990 5% PUMS file, the paper compares the assimilation of selected Chinese immigrant cohorts, based upon age and period of entry. Including a spatial component within the framework, we examine whether differences in the organisation and assimilation of immigrant groups exist across space. For each cohort, contrasts are made with reference to location in the New York Consolidated Metropolitan Statistical Area (CMSA), with the analysis focusing upon differences in spatial assimilation with respect to acculturation, socioeconomic characteristics, internal migration, and immigrant characteristics relative to other immigrant and native‐born groups. The analysis is updated using Immigration and Naturalization Service (INS) data files from the 1990s. Results suggest that space, and location in space, alter the assimilation trajectory of similarly defined groups. Copyright © 2004 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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.278
Teacher spread0.249 · 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