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Record W4386536412 · doi:10.32920/24085095.v1

Planning with multicultural diversity in small cities in Canada: a case study of immigrant lived-experiences in Brooks, Alberta

2023· preprint· en· W4386536412 on OpenAlexaffabout
Ryan Lok

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsToronto Metropolitan UniversityUniversity of Calgary
Fundersnot available
KeywordsImmigrationMulticulturalismDiversity (politics)Settlement (finance)Ethnic groupCultural diversityPolitical scienceEconomic growthSociologyBusiness

Abstract

fetched live from OpenAlex

<p>Immigration, ethnic settlement, and global migration are processes which shape diversity in Canadian cities. There is rich literature vis-à-vis immigrant settlement in large, gateway cities. Less is known about the lived-experiences of visible-minority immigrants in small cities and the implications of planning with diversity in those contexts. This research explored the challenges and opportunities of visible-minority immigrants settling in a small city from a place-based and integration perspective and explored the role of municipalities in attracting and retaining immigrants in small cities. Case study research of Brooks, Alberta was conducted involving interviews with visible-minority immigrants, a municipal official, and a local immigration organization staff. This study had reinforced the primary reason of immigrant settlement in small cities were based on economic and family-related factors, but highlight the role of the municipality in retaining immigrants in small cities is to cultivate an inclusive community by acclaiming diversity through a place-based approach.</p> <p><br></p> <p>Key Words: Multicultural Planning, Municipal Policy Diversity, Immigrant Lived-Experiences, Small Cities, Attract and Retain Immigrants, Visible-Minority Immigrants</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.231
Teacher spread0.172 · 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.

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
Published2023
Admission routes2
Has abstractyes

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