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Record W4362241803 · doi:10.1353/ces.2023.0006

Middle-Class Nation-Building through Immigration?

2023· article· en· W4362241803 on OpenAlexvenueaboutno aff
Friederike Alm, Derek J. Robey

Bibliographic record

VenueCanadian ethnic studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMiddle classCitizenshipPolitical scienceImmigration policySociologyEconomic historyGender studiesHistoryLawPolitics

Abstract

fetched live from OpenAlex

Middle-Class Nation-Building through Immigration? Friederike Alm (bio) and Derek J. Robey (bio) September 21-23, 2022 Canada Program at the Weatherhead Center for International Affairs, Harvard University The workshop "Middle-class Nation-building through Immigration?" was organized under the auspices of Professor Elke Winter, who held the position of William Lyon Mackenzie King Visiting Professor of Canadian Studies at the Canada Program of the Weatherhead Research Cluster on Comparative Inequality and Inclusion at Harvard University from 2019-2021.1 It took place from September 21 to 23, 2022 at Harvard University.2 There were seven sessions, each reserved for the presentation of one paper, followed by comments and insights by other eminent researchers. The workshop was held to (1) present contributions to the homonymous Special Issue to be published in the Journal of Ethnic and Migration Studies, (2) discuss the notion of middle-class nation-building through immigration as a strategic approach by immigrant-receiving countries, and (3) apply a comparative perspective on this topic by integrating case studies beyond the Canadian case, including the United Kingdom, Germany, and Australia, among others. Major immigrant receiving countries continue to grapple with their approaches to immigration with increasing urgency. The emerging trend, highlighted by Professor Elke Winter, is an immigrant selection strategy that couples immigration and citizenship policies with the maintenance and strengthening of the middle-class. This strategy allows immigrant-receiving countries to reap the benefits of high human capital immigration while also actively engaging in nation-building through immigration. The contributors to the Special Issue have approached the notion of middle-class nation-building through immigration by discerning three underlying [End Page 147] themes along which the workshop was structured: The Race for Talents and Its Discontents, Migration and Neoliberalism, and Social Inequality and Polarizations. The Race for Talents and Its Discontents Session I began with an empirical study of immigrant selection preferences by social class and origin in Québec, Canada. In their research paper, Immigration to Build the Nation, Not to Transform it: Preferences in Immigrants' National Origin and Social Classes in Quebec, authors Antoine Bilodeau and Audrey Gagnon presented an elaborate survey experiment research design that investigates how Quebecers would select potential immigrants if faced with the country of origin and the profession (as the operationalization of social class) of an individual immigration applicant. The results of their research indicate that social class moderates the evaluation gap based on national origin when the immigrant's linguistic ability (fluent French) is kept constant. This indicates a relative degree of openness towards newcomers with the potential to economically contribute to society. Commenters found the methodological design specifications impressive and the empirical analysis compelling, though there were differences in how the results were interpreted. One commenter (Mathieu Lizotte) argues the findings suggest national origin matters relatively little, contradicting extant research that emphasizes how members of a receiving society strongly prefer immigrants who match their own ethnocultural backgrounds. This interpretation invites the authors to consider additional factors that might explain patterns in evaluation of immigrant profiles such as the evaluator's socioeconomic status. Another commenter (Natasha Warikoo) notes the experimental survey design frames immigration decisions in a non-zero-sum manner, which could explain why the evaluation gaps based on national origin and class background are relatively small. The COVID-19 pandemic shifted our collective understanding of skill and essential work, which also affected the discussion on immigrant workers who tend to be overrepresented in essential work sectors like health and care work. This issue was taken up in Yasmeen Abu-Laban's paper, Race for Talent: Immigrants, Refugees, and the Tenacity of a Discourse on Skills in Canada in Session II. The author traces the historical discourse on skills in Canadian immigrant selection, going back to the 1960s. The discursive focus on skills in immigration, she argues, was introduced as a neutral tool to de-center immigrant selection from race-based criteria in the points system. However, she points to the early conflation of skills-based criteria with refugee selection, which was abandoned after international criticism but has now reemerged through the 2018 Economic Mobility and Pathways Project. This persistent conflation of humanitarian...

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.640
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.231
GPT teacher head0.397
Teacher spread0.166 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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