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Record W2460762555 · doi:10.1007/s13644-016-0252-7

Christian Churches and Immigrant Support in Canada: An Organizational Ecology Perspective

2016· article· en· W2460762555 on OpenAlexafffundabout
Sam Reimer, Mark D. Chapman, Rich Janzen, James Watson, Michael Wilkinson

Bibliographic record

VenueReview of Religious Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsTrinity Western UniversityCentre for Community Based ResearchTyndale UniversityWestern UniversityCrandall University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImmigrationGovernment (linguistics)Service (business)Perspective (graphical)Settlement (finance)SociologyReligious organizationWork (physics)Public relationsEcologyService providerPublic administrationEconomic growthPolitical scienceBusinessMarketingLawEconomicsBiology

Abstract

fetched live from OpenAlex

Canada receives roughly 250,000 immigrants each year, and the government spends considerable resources on assisting them to settle and integrate into Canadian society through the agencies they support. Most of these new immigrants settle in Canada's largest cities, where churches meet specific needs that extend beyond the capacities of government agencies. In smaller centers, churches cover a wide range of services because few government supports are available. Little is known about the work of churches in Canada in spite of their importance to immigrant settlement and integration. In this study, we examine the services offered to immigrants by Canadian Christian churches. We show how the service provision of Christian churches is constrained by other organizations and groups in their environment, in ways consonant with the organizational ecology framework. Specifically, churches service the needs of immigrants by adapting to specific niche needs and by filling in gaps left by other service providers.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.090
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0120.010
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.371
Teacher spread0.338 · 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

Citations39
Published2016
Admission routes3
Has abstractyes

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