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Record W1969407876 · doi:10.1177/0042098013502826

Indigeneity, Immigrant Newcomers and Interculturalism in Winnipeg, Canada

2013· article· en· W1969407876 on OpenAlexaffabout
John Victor Gyepi-Garbrah, Ryan Walker, Joseph Garcea

Bibliographic record

VenueUrban Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousInterculturalismImmigrationUrbanismSociologyGender studiesRacismSocioeconomic statusColonialismPolitical scienceEconomic growthGeographySocioeconomicsMulticulturalismPopulationEcology

Abstract

fetched live from OpenAlex

This paper examines how modern urban Indigeneity is influencing the integration of immigrant newcomers in Western settler cities. Using a case study of Ka Ni Kanichihk Inc. (KNK), an Indigenous organisation in the city of Winnipeg, Canada, this research contributes to the emerging framework of intercultural urbanism. Indigenous peoples and newcomers are living side-by-side in many neighbourhoods, with common histories of colonialism, racism and socioeconomic challenges. Interviews with staff and focus groups with Indigenous and newcomer participants of KNK programmes indicated that they are beginning their co-existence, mostly in inner-city neighbourhoods, with low levels of interaction, mutual misunderstanding, misperceptions, segregation and tension among youth in high schools. Through the initiatives of KNK and partner organisations, cross-cultural understanding and relationships are being built, overcoming social distance. There is great potential for building intercultural relationships among Indigenous peoples and immigrant newcomers as a means of decolonising Western cities.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0170.005
Scholarly communication0.0040.001
Open science0.0010.004
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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 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

Citations39
Published2013
Admission routes2
Has abstractyes

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