MétaCan
Menu
Back to cohort
Record W2982448002

City-suburban differences in government responses to immigration in the greater Toronto area

2000· article· en· W2982448002 on OpenAlexaboutno aff
Marcia Wallace, Frances Frisken

Bibliographic record

VenueTSpace (University of Toronto) · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationGovernment (linguistics)GeographyPolitical scienceEconomic growthDemographic economicsSocioeconomicsSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Immigration is a national government responsibility in most countries, and for that reason its effects on the behaviour of municipal governments have received little attention. This paper focuses on immigration into the urban and suburban cities of the Greater Toronto Area, and examines how six cities in particular respond to their immigrant communities. The research found that, despite functioning within a common legislative and economic context, and having similarly large percentages of their population as immigrants, the responses of municipal governments to immigrant settlement vary not only in content and comprehensiveness, but also in the amount of initiative shown by municipal officials in putting the responses in place. These variations suggest that Canadian municipal governments have more flexibility to design their own policies than is implied by their constitutionally mandated subjection to provincial laws. This may be especially true for those circumstances, of which immigrant settlement is one, where the scope and intent of senior government policies are unclear or are undergoing frequent modifications.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.022
GPT teacher head0.256
Teacher spread0.233 · 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.

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

Citations16
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicMigration, Refugees, and IntegrationFrench-language works237,207