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Record W6923601508 · doi:10.14288/1.0435877

Motor City Park : Reimagining privately owned public spaces as sites of collaborative self expression

2023· article· en· W6923601508 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2023
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsAmenityContext (archaeology)Public spaceEconomic rentPublic open spacePlan (archaeology)Space (punctuation)Urban planning

Abstract

fetched live from OpenAlex

Ongoing development pressure in the City of Vancouver has resulted in residents being priced out of neighbourhoods where they used to live and work. The proposed Broadway Plan calls for intensive upzoning over several neighbourhoods which currently contain some of the most affordable rents in the city, without planning for adequate public space needed for increased density. The city is to rely on private developers to provide public benefits through CACs (Community Amenity Agreements). Frequently, these agreements lead to the formation of Privately Owned Public Spaces (POPS), wherein private owners get to design and manage the space, often to the exclusion of the public. This project suggests a shift from the current status quo, instead giving this right to community groups to support creative expression. The former MEC flagship building at 130 West Broadway was chosen as the test site. Working within the context of this new development proposal allowed for the visualization of environmental changes that may take place on site. The design resolution sought to maximize light into the space, while connecting the street level to the higher elevation at the back lane, while also incorporating the use of indoor space for studios.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.004
Scholarly communication0.0080.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.190
Teacher spread0.174 · 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
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
Published2023
Admission routes1
Has abstractyes

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