MétaCan
Menu
Back to cohort
Record W2014787886 · doi:10.1080/02722011.2013.819367

The Implications of Immigration Federalism for Non-citizens’ Rights and Immigration Opportunities: Canada and Australia Compared

2013· article· en· W2014787886 on OpenAlexaboutno aff
Sasha Baglay, Delphine Nakache

Bibliographic record

VenueThe American Review of Canadian Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFederalismResidenceImmigration policyCitizenshipState (computer science)Political scienceScholarshipImmigration lawCultural assimilationEconomic growthPublic administrationDemographic economicsEconomicsLaw

Abstract

fetched live from OpenAlex

This article explores the development of immigration federalism in Australia and Canada (expressed through the establishment of state/provincial/territorial immigrant selection programs) and its implications for immigrants’ rights and immigration opportunities. Given the very limited scholarship on the issue, and the lack of previous comparative studies on immigration federalism in Australia and Canada, our research is exploratory by nature. Our finding is that provincial/state/territorial programs offer some advantages to prospective applicants (such as increased immigration opportunities), but, at the same time, raise a number of concerns (such as an increased dependence on employers). As our study reveals, the costs and benefits of immigration opportunities under state/provincial/territorial programs differ for skilled and low-skilled workers, whereby the latter are given only limited access to permanent residence, and on more onerous conditions than skilled workers. Drawing on these findings, we identify areas in need of further research and policy response.

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)0.006
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.012
Science and technology studies0.0080.005
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.067
GPT teacher head0.342
Teacher spread0.274 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe American Review of Canadian StudiesSame topicMigration, Refugees, and IntegrationFrench-language works237,207