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Record W1986820338 · doi:10.1177/1078087404273443

The Construction of the Geography of Immigration as a Policy Problem

2005· article· en· W1986820338 on OpenAlexaffabout
Yasmeen Abu‐Laban, Judith A. Garber

Bibliographic record

VenueUrban Affairs Review · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImmigrationCensusSettlement (finance)NewspaperImmigration policyBiological dispersalGovernment (linguistics)Construct (python library)GeographySociologyEconomic growthPolitical sciencePublic administrationLawEconomicsDemographyPopulation

Abstract

fetched live from OpenAlex

The release of 2000 U.S. Census and 2001 Canadian Census data sparked significant interest in immigrant dispersal outside major urban centers. This article analyze show the meaning of immigration settlement patterns is socially constructed by using a comparative textual analysis of newspaper coverage of census findings as well as government documents and think tank studies. The authors argue that in Canada, immigration settlement is interpreted as a national policy problem necessitating federal state intervention, whereas presentations in U.S. print media construct immigration settlement as the outcome of choices made by individual immigrants and, thus, as local policy problems. In each case, construction of immigrant dispersal draws on national mythologies and omits alternative interpretations of the geography of immigrant settlement.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0080.068
Scholarly communication0.0150.010
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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 designNot applicable
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

Citations52
Published2005
Admission routes2
Has abstractyes

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