MétaCan
Menu
Back to cohort
Record W4247926066 · doi:10.32920/ryerson.14640159.v1

The Private sector, institutions of higher education, and immigrant settlement in Canada

2021· preprint· en· W4247926066 on OpenAlexaboutno aff
Emma Flynn

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Devolution (biology)ImmigrationNeoliberalism (international relations)Higher educationPublic administrationPolitical scienceService (business)Private sectorEconomic growthBusinessEconomicsSociologyFinanceLawMarketing

Abstract

fetched live from OpenAlex

The settlement sector in Canada has undergone significant transformations in recent times, most notably the imposition of neoliberal principles on service providers that has transferred a substantial amount of the immigrant selection and recruitment process from governmental agencies to third parties. This trend of devolution has accelerated with recent developments associated with Provincial Nominee Programs. By reviewing the literature related to Provincial Nominee Programs and their implementation, we illustrate how private employers and institutions of higher education are not only involved in immigrant selection but also increasingly in settlement service delivery. Keywords: immigration, settlement services, Provincial Nominee Program, neoliberalism, privatization, institutions of higher education, 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0120.005
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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 designObservational
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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicMigration, Ethnicity, and EconomyFrench-language works237,207