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Record W3001933913 · doi:10.1111/cag.12597

Placing the second generation: A case study of Toronto

2020· article· en· W3001933913 on OpenAlexafffundvenueabout
Valerie Preston, Brian Ray

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMicrodata (statistics)First generationMetropolitan areaCensusImmigrationGeographyDemographic economicsThird generationPublic housingDemographySocioeconomicsSociologyPolitical sciencePopulation

Abstract

fetched live from OpenAlex

We examine the social mobility of the second generation in the Toronto metropolitan area by analyzing whether the adult children of immigrants live in more affluent and desirable neighbourhoods than the first generation. Using 2016 census microdata, we compare the social characteristics of census tracts where immigrants and the second and third‐plus (3 + ) generations concentrate. The index of dissimilarity indicates the degree of residential separation among generations and for five ethno‐racial second‐generation groups: Chinese, South Asian, Black, Southern European, and Northern and Western European. The empirical findings show that the neighbourhoods where the first generation is over‐represented are less affluent than those where the second and 3+ generations concentrate, with the largest improvements in social status occurring between the first and second generations. Although they frequently live in more distant suburban neighbourhoods than the first generation, the second generation still tends to live in inner and outer suburbs more than the exurban 3+ generation. For the second generation, the degree of residential concentration varies across ethno‐racial groups with persistent segregation marking the residential locations of racial minorities. The findings highlight the variegated geographies and social mobility of the second generation in Canada's largest metropolitan area .

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)0.000
metaresearch head score (Gemma)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.241
Teacher spread0.211 · 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

Citations6
Published2020
Admission routes4
Has abstractyes

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