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American Migration, Settlement, and “Belonging” in Francophone Canada

2014· article· en· W2060150776 on OpenAlexaboutno aff
Susan W. Hardwick

Bibliographic record

VenueGeographical Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsFrenchImmigrationContext (archaeology)CensusPopulationPoliticsSettlement (finance)Canadian studiesHistoryEthnologyGender studiesSociologyGeographyGenealogyPolitical scienceMedia studiesDemographyLaw

Abstract

fetched live from OpenAlex

More Americans now reside in Canada than at any time since the Vietnam War. Of particular note is the surprisingly large population of immigrants from the United States who now reside in Montreal—Francophone Canada's largest and most diverse city. This article documents and analyzes the migration experiences, spatial patterns, and “sense of belonging” of Americans in Montreal during the post–Vietnam era framed within the larger political and linguistic context of the city's “Two Solitudes.” Findings are based on information compiled from archival materials, census records, structured and unstructured interviews, survey questionnaires, participant observation, and fieldwork. My overarching goal is to embed the experiences and patterns of this English‐speaking group of immigrants in predominately French‐speaking Montreal during the past five decades—one of the most dramatic and divisive periods of time in Montreal and in Quebec as a whole.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0150.005
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.226
Teacher spread0.222 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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