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Record W4235639475 · doi:10.32920/ryerson.14646456

The purpose of public art in the Canadian suburb: an evaluation of Markham's public art program

2021· preprint· en· W4235639475 on OpenAlexaffabout
Rayson Wong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPlacemakingEnthusiasmPublic relationsPublic artProject commissioningVariety (cybernetics)Political scienceUrban planningSociologyPublic administrationPublishingUrban designEngineeringVisual artsPsychologyCivil engineeringArtLawComputer science

Abstract

fetched live from OpenAlex

Public art is a creative placemaking tool to enhance the quality of civic life and foster a sense of community. There is growing enthusiasm for public art to be integrated into the suburban environment in fostering a more culturally vibrant place. This paper explores the unique challenges faced in suburban public art planning. The City of Markham’s new public art program is used as a case study. Successful public art in the suburb should reflect the local community’s history, values, or needs. Public engagement and collaboration is critical to creating public art that garners intrinsic connections. Generally, since suburban municipalities have smaller populations and lower developmental demand than urban cores, they should incorporate a variety of funding tools to effectively sustain their public art programs. Markham should increase its efforts on engaging the public in all aspects of public art commissioning, and maximize their financial resources in order to increase the presence of its program.

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)0.011
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.607

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0130.004
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.154
GPT teacher head0.404
Teacher spread0.251 · 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 routes2
Has abstractyes

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