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Record W2556967335

What Could Possibly Go Wrong? Examining the Consequences of the City of Toronto Public Square By-laws on Diversity in Yonge-Dundas Square

2014· article· en· W2556967335 on OpenAlexaboutno aff
Kecil Joseph

Bibliographic record

VenueQSpace (Queen's University Library) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsSquare (algebra)Diversity (politics)LawPolitical scienceMathematicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

Public spaces are indispensible to the success of life in a city. Irrespective of ones opinion on whether the space is truly accessible or would be better off privately managed, at the most fundamental level, there is an overwhelming consensus, that public spaces shape the way that communities and neighborhoods mesh together. However, it is not enough to just build public spaces in the city. There are important characteristics that must be adhered to, in order for such spaces to be truly accessible to everyone, and to allow for diversity of activities, individuals and ideas to be encountered and experienced. But in an era where local municipalities often face financial budgetary constraints, coupled with the increasing role of the private sector, there are now new forms of public space management that are bringing forth newly enhanced regulations and local by-laws. The outcome is that such spaces are becoming less public as a result of exclusion of certain behaviours activities and political practices. \nToronto’s Yonge-Dundas Square, which is often compared with the likes of New York’s Times Square is managed by a board of management, who is tasked with implementing a set of by-laws that excludes "undesirables" and influences behaviors and activities occurring at the square. The purpose of this report is to identify what by-laws affect Yonge-Dundas Square public space in the City of Toronto and to determine what consequences they have on the diversity of users interacting and utilizing the space. Second, this report seeks to determine if there is an inconsistency with the intent of the by- laws and the reality of how the by-laws are interpreted by those who are implementing them.

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)0.007
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 score0.058
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.006
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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
Published2014
Admission routes1
Has abstractyes

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