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Record W4388490765 · doi:10.59490/abe.2017.12.3619

Diversity, public space and places of encounter:

2018· article· en· W4388490765 on OpenAlexaboutno aff
Donya Ahmadi

Bibliographic record

VenueArchitecture and the Built Environment · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Diversity (politics)Public spacePerceptionSpace (punctuation)SociologyCultural diversityPublic relationsPolitical scienceGeographyPsychologyEngineeringComputer scienceAnthropology

Abstract

fetched live from OpenAlex

Increasingly, public spaces are being regarded as important resources for fostering multi-cultural coexistence and for creating opportunities for cross-cultural understanding and dialogue, in that they can provide a platform wherein interactions across diverse backgrounds occur. This article explores the perceptions of public place in a highly diverse, post-war, modernist suburb of Toronto, and the extent to which public spaces play a role in fostering interactions between different groups and catering for diversity in the area. The analysis indicates that there is little evidence for encounters between diverse groups in public spaces, due to the lack of spatial infrastructure anticipated in the modernist design of the neighbourhood. In addition, social factors such as surveillance and policing, lack of appropriate symbols that cater to different user groups, and presence of gangs and violence have resulted in residents’ self-exclusion from public spaces and undermined the frequency and quality of their social encounters.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0110.022
Scholarly communication0.0130.006
Open science0.0010.014
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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