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Record W4247242241 · doi:10.32920/ryerson.14636169

Discounting Immigrant Families: Neoliberalism and the Framing of Canadian Immigration Policy Change: A Literature Review

2021· review· en· W4247242241 on OpenAlexfundaboutno aff
Jesse Root, Erika Gates-Gasse, John Shields, Harald Bauder

Bibliographic record

Venuenot available
Typereview
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)ImmigrationNeoliberalism (international relations)Immigration policyMulticulturalismSociologySocial policyConceptual frameworkPolitical scienceGeneral partnershipGender studiesPolitical economySocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract This paper aims to develop a conceptual framework to assist in understanding how the immigrant family is impacted by recent changes to immigration policy in Canada. We contend that neoliberalism, broadly defined, is a helpful lens through which to comprehend some of the specific policies as well as discursive outcomes which have real effects on immigrant families. Based on our findings from an in-depth literature review, our goal is to identify and summarize the recent changes to the Canadian policy environment and to develop a critical conceptual framework through which to understand policy change in relation to families and immigrants. Key Words: families, neoliberalism, policy change, social policy, multiculturalism, gender, race, neoconservatism Acknowledgements The research for this paper was supported by a Partnership Development Grant titled “Integration Trajectories of Immigrant Families” by the Social Sciences and Humanities Research Council of Canada.

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)0.010
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.173
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.023
Science and technology studies0.0040.005
Scholarly communication0.0070.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.380
Teacher spread0.327 · 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".

Quick stats

Citations6
Published2021
Admission routes2
Has abstractyes

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