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Record W2894660352

Planning For Privately Owned Public Space In The Greater Toronto Area

2018· article· en· W2894660352 on OpenAlexaboutno aff
Nicholas Wood

Bibliographic record

VenueYork University Digital Library (York University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsPublic spaceSpace (punctuation)BusinessArchitectural engineeringComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Privately Owned Public Spaces (POPS) are public spaces which allow general access but remain under the ownership of the property owner. While popular around the world, they are becoming a visible entity in the landscapes of the Greater Toronto Area (GTA). The presence of POPS in downtown Toronto has been visible for several decades; however, cities located in the periphery of Toronto are beginning to adapt and create policies which allow for the creation of POPS. Although this integration is at its initial stage, with few spaces constructed and minimal policy preparation, there is a conscious intention to add this type of public space into the network of parkland and open space. Through interviews conducted with municipal planners, this research provides insight into the rationale and motivations of these cities to understand what is driving their pursuit of POPS. This research will contribute to the discussion on POPS from the context of an emerging market. As well, this paper offers recommendations which can assist municipalities preparing policies to regulate POPS.

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)0.001
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 score0.322
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.049
GPT teacher head0.217
Teacher spread0.168 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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