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Record W2022763289 · doi:10.3138/cpp.36.2.241

A Decade Later: Assessing Successes and Challenges in Manitoba's Provincial Immigrant Nominee Program

2010· article· en· W2022763289 on OpenAlexaffvenueabout
Nathaniel M. Lewis

Bibliographic record

VenueCanadian Public Policy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsImmigrationSettlement (finance)InequalityPolitical scienceNew immigrantsService (business)Public administrationGeographyEconomic growthBusinessLawEconomicsMarketingFinance

Abstract

fetched live from OpenAlex

During the past decade, Manitoba's Provincial Nominee Program (MPNP) has increased immigration to the province and dispersed immigrants more widely within Manitoba. At the same time, the rapid growth of the program and the decentralized way in which it has been implemented have contributed to some challenges. This ten-year analysis of the MPNP finds that many places in Manitoba are experiencing settlement service gaps, and that immigrants and communities are taking on much of the burden for MPNP application and settlement. In addition, the analysis demonstrates that the fragmented way in which the MPNP has been marketed and implemented (i.e., by relying on particular employers, consultants, and ethnocultural organizations) has resulted in a sort of ethnocultural inequality where certain groups are ushered into the province-often to perform particular occupations-while others are bypassed.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.418
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.385
Teacher spread0.302 · 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.

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

Citations37
Published2010
Admission routes3
Has abstractyes

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