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Record W4239816157 · doi:10.32920/ryerson.14655417.v1

The "school of life" : differences in U.S. and Canadian settlement policies and their effect on individual immigrants' experience

2021· preprint· en· W4239816157 on OpenAlexaffabout
Annie Laurie Duguay

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsSettlement (finance)ImmigrationPolitical sciencePsychologySociologyBusiness

Abstract

fetched live from OpenAlex

A growing body of literature suggests that language proficiency in the main language of the destination country is one of the most significant factors in the integration of immigrants. This study examines the overall differences in U.S. and Canadian settlement policy, using the provision of language courses as an example of the ways in which adult immigrants are integrated into the host society. Eleven Haitian women in both countries were interviewed to compare the way in which participants accessed key settlement information and resources as well as their language acquisition. The findings reveal that Canadian-based participants were much more likely to cite professional institutions ("formal facilitators") for referrals, whereas American-based particpants were more likely to learn from "informal facilitators." The findings also highlight differences in access and completion rates of language classes. Implications for how national settlement policy affects individual immigrants and their language acquisition are analyzed in the discussion.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.035
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0140.006
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.049
GPT teacher head0.384
Teacher spread0.335 · 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
Published2021
Admission routes2
Has abstractyes

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