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Record W7161614121

Changing Faces, Changing Neighbourhoods : Government Assisted Refugee Settlement Patterns in Metro Vancouver January 2005 – December 2009

2010· report· en· W7161614121 on OpenAlexafffundabout
Lisa Ruth Brunner, Chris Friesen, Kathy Sherrell

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typereport
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
FundersMitacsSimon Fraser University
KeywordsRefugeeImmigrationSettlement (finance)Government (linguistics)PopulationDisplaced person
DOInot available

Abstract

fetched live from OpenAlex

Over 4,000 government assisted refugees were resettled to British Columbia between 2005 and 2009 from close to 50 different countries. Although these refugees have tremendous life experiences and resilience, since the new Immigration and Refugee Protection Act was passed in June 2002, an increasing number arrive in Canada with significant settlement and adjustment challenges due to protracted refugee camp situations, various medical conditions, illiteracy, lack of family support, lack of English or French language skills, etc. The purpose of this report is to highlight some of the settlement patterns and trends of government assisted refugees settling in Metro Vancouver, based on the mapping of a snapshot of their postal codes taken in April 2010. It is our hope that this information will both increase the level of knowledge of government assisted refugees in the broader community as well as assist community based agencies, public institutions, and the three levels of government to explore the need for enhanced supports to this refugee population in British Columbia.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.075
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.218
Teacher spread0.204 · 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

Citations0
Published2010
Admission routes3
Has abstractyes

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