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Record W1605073087

Summary Of: The Initial Destinations and Redistribution of Canada's Major Immigrant Groups: Changes over the Past Two Decades

2005· preprint· en· W1605073087 on OpenAlexaffabout
Feng Hou

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsImmigrationDestinationsRedistribution (election)Demographic economicsCitizenshipGeographyBiological dispersalDistribution (mathematics)Human settlementPolitical sciencePopulationDemographySociologyEconomicsTourism
DOInot available

Abstract

fetched live from OpenAlex

This article summarizes findings from the research paper entitled: The Initial Destinations and Redistribution of Canada's Major Immigrant Groups: Changes over the Past Two Decades. In 1981, about 58% of immigrants who had come to Canada in the previous 10 years lived in Toronto, Vancouver, and Montreal; by 2001, this had increased to 74% (Statistics Canada 2003), triggering debate on the merits of a more 'balanced geographic distribution of immigrants' (Citizenship and Immigration Canada-CIC 2001). Policies aimed at directing immigrants away from major gateway cities in many western countries have focused on the choice of initial destination, and little effort has been made to affect subsequent mobility. But such policies will work only if other, non-gateway regions, can keep immigrants or maintain balanced in- and out-migration. To this end, this study examines how Canada's major immigrant groups arriving over the past two decades have altered their geographic concentration through time, comparing immigrants arriving in the 1970s, 1980s, and 1990s, in the concentration levels of their initial destinations, and in their subsequent geographic dispersal. It pays attention to the dispersal pattern of groups whose initial settlements were influenced by government policies and questions the role of pre-existing immigrant communities in geographic distribution.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.331
Teacher spread0.307 · 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

Citations19
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicMigration and Labor DynamicsFrench-language works237,207