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Record W4291728210 · doi:10.3138/cpp.2022-010

Navigating Uncertainties: Evaluating the Shift in Canadian Immigration Policies during the COVID-19 Pandemic

2022· article· en· W4291728210 on OpenAlexaffvenueabout
Ashika Niraula, Anna Triandafyllidou, Marshia Akbar

Bibliographic record

VenueCanadian Public Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicImmigration2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceImmigration policyDevelopment economicsVirologyEconomicsMedicineLawOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Canada has a proactive immigration policy that invites individuals, mostly highly skilled ones, from around the world to make it their new home. The pandemic border closures severely affected the flow of immigrants from other countries, so the Canadian government turned to the temporary migrants who were already in the country and facilitated their transition to permanent status. Reviewing the relevant policy documents and analysing 22 semi-structured qualitative interviews with stakeholders in Ontario, we critically examine the impact of two transition measures: the amendments to Express Entry and the Temporary Residence to Permanent Residence Pathway Program. We also discuss the changes in the work permit program for international graduates. Moreover, we analyse Canadian migration management during the pandemic at three levels: the macro level (i.e., transition measures and attainment of national goals), the meso level (i.e., stakeholders' evaluations of the transition measures), and the micro level (i.e., stakeholders' perceptions of migrants' experiences with the transition measures).

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0080.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.383
Teacher spread0.325 · 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.

Study designNot applicable
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

Citations13
Published2022
Admission routes3
Has abstractyes

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