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Record W3210211007 · doi:10.32920/ryerson.14668236.v1

The role of ethnic malls in placemaking: a case study of First Markham Place

2021· preprint· en· W3210211007 on OpenAlexaff
Philip L.‐F. Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEthnic groupPlacemakingSpace (punctuation)Sense of placePublic spacePublic goodSense of communitySociologyPublic relationsAdvertisingGeographyPolitical scienceArchitectureSocial scienceBusinessAnthropologyEngineeringUrban designArchaeologyEconomics

Abstract

fetched live from OpenAlex

This study examines the role and effectiveness of suburban, ethnic shopping centres in providing an alternative to public space. It is a response to the suburb's lack of good public spaces, and the resulting lack of community and sense of place, and is informed by the development of 'ethnoburbs' across North America. This study explores themes revealed by both literature and a series of field observations and intercept interview. A case study analyzing First Markham Place and how its mall patrons use the space revealed implications regarding the effectiveness of these malls as public spaces. The author found that the mall's role as a community hub provides opportunities to satisfy both practical and innate desires for cultural goods, services, and co-ethnic interactions, encourages a 'public life' not seen in conventional suburban malls, and creates a unique sense of place for members of the target ethnic community as well as non-members.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.965
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0250.008
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.045
GPT teacher head0.335
Teacher spread0.290 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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