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Record W2903511316 · doi:10.36939/cjur/vol26no2/art99

Immigration and the City by Eric Fong and Brent Berry

2017· article· en· W2903511316 on OpenAlexvenueaboutno aff
Carlos Teixeira

Bibliographic record

VenueCanadian journal of urban research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationBerryPolitical scienceSociologyLawBiologyBotany

Abstract

fetched live from OpenAlex

Cultural and racial heterogeneity is one of the defi ning characteristics of the immigration that has shaped Canada and the United States, particularly in the countries' major urban centres.In recent decades, the source countries of immigration have also changed, from continental Europe to non-European countries (e.g., Asia, Latin America, Africa, the Caribbean and the Middle East).Socio-economic disparity among immigrants is another characteristic of this "new" wave of immigration.Both "old" and "new" immigration waves have played a key role in shaping the richly complex social, economic, religious, and political landscape of both countries' urban and suburban landscapes.Today, both countries' major gateway cities are characterized by a multiethnic mix of immigrant groups settled in a diverse array of neighbourhoods and diaspora communities.Th e latter provide excellent social laboratories to study "immigration and the city," and to observe how immigration, and its growing ethnic and racial diversity, aff ects urban structures and processes, including their "ethnic imprint" left on cities.However, despite the fact that immigrants and their descendants form an important segment of the total population of the major metropolises in the United States and Canada, comparative scholarly work on the rich history of their settlement experiences, community formation/structure and their impact in major "gateway cities" remains extremely limited (see the most recent work by: Qadeer 2016; Teixeira, Li and Kobayashi 2012 and Frazier, Darden and Henry 2009).In the "age of migration" this timely book by Fong and Berry thus makes an important contribution to the study of immigration in cities in the United States and Canada.As the authors note, this book largely focusses on the settlement and acculturation of immigrants in both countries.More specifi cally, it explores how geography shapes settlement in cities and key aspects of immigrant housing attainment; community, business, and economic activity; and contributions to cosmopolitan life.Th e social and economic life of immigrants, including their integration in cities is an increasingly important topic.How urban forms shape immigrants' integration patterns and how their adaptation changes these forms are key questions guiding this study.In answering them, Fong and Berry provide an invaluable perspective on how immigrants shape the structure of cities and how these cities, in turn, accommodate the immigrants' culturally diverse needs and preferences.Drawing insight from a rich multidisciplinary literature spanning migration and ethnic studies, social and cultural geography, economic and urban sociology, this well-structured, comprehensive manuscript off ers an in-depth understanding of the working of immigrant urban communities, institutions and businesses.Th e book begins by defi ning the main characteristics defi ning contemporary immigration to major cities in the United States and Canada, as well as the characteristics of the urban context where immigrants settle.Th e authors then proceed to a summary of the classical sociological explanations/perspectives in the early twentieth century and explore how new scholarly perspectives/explanations attempt to address some of the limitations

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0090.003

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 head0.088
GPT teacher head0.363
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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