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Record W4394812766 · doi:10.1080/15562948.2024.2335952

Macroeconomic Impacts of Immigration in the Canadian Atlantic Region: An Empirical Analysis Using the FOCUS Model

2024· article· en· W4394812766 on OpenAlexafffundabout
Peter Dungan, Tony Fang, Morley Gunderson, Steve Murphy

Bibliographic record

VenueJournal of Immigrant & Refugee Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMemorial University of NewfoundlandUniversity of Toronto
FundersNational Social Science Fund of ChinaSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsImmigrationPer capitaDemographic economicsGovernment (linguistics)Econometric modelEconomicsGovernment spendingPolitical scienceDevelopment economicsEconometricsDemographySociology

Abstract

fetched live from OpenAlex

We simulate the impact of an increase in immigration into the Atlantic Provinces based on the FOCUS macro-econometric model at the University of Toronto. That national model was adapted to reflect the regional dimensions of the Atlantic Provinces. We find robust evidence of positive outcomes for the Atlantic region so long as it is part of a broader increase in immigration for the country as a whole. The positive outcome encompasses higher GDP and GDP per capita, higher consumption, and improved government fiscal balances at both the federal and provincial levels that could in turn be used for tax reductions or the enhancement of government services. These benefits could be enhanced further by carefully targeting new immigrants for needed skills and for their likelihood of remaining in the Atlantic region.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.388
Teacher spread0.331 · 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 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

Citations0
Published2024
Admission routes3
Has abstractyes

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