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Record W4238098802 · doi:10.32920/ryerson.14660910

A recommendation for integrative public space in Lawrence Heights

2021· preprint· en· W4238098802 on OpenAlexaffabout
Laura Costa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNeighbourhood (mathematics)RedevelopmentPlan (archaeology)Function (biology)Space (punctuation)Variety (cybernetics)Adaptation (eye)Public spaceProcess (computing)Public relationsForm and functionSociologyArchitectural engineeringPolitical scienceComputer scienceGeographyEngineeringPsychologyCivil engineering

Abstract

fetched live from OpenAlex

Cities are never static; they are ever-evolving organisms requiring constant adaptation in an effort to function for the changing and increasingly diverse populations. One of the main issues with past planning practices in the development of social housing in Ontario is the lack of integrative strategies used to build neighbourhoods. Defining what successful integration entails is an important aspect of the creation of successful communities. The purpose of this study is to understand the elements necessary for creating integrative public space for Lawrence Heights; a community located in Toronto that will undergo an extensive revitalization process over the next twenty years. To determine the results, personal observations of the neighbourhood were conducted in a few ways: by taking photographs and notes of activities being enjoyed in the area, by attending a public meeting to comprehend the proposed plan for the redevelopment, and by participating in a workshop to understand the opinions of residents who will be affected by change in the neighbourhood. Integrative public space should include programs, facilities and multi-use, flexible aspects that cater to a variety of people, and can be easily accessed by users. Public space is only one component of integration, but is a very important one to comprehend in the practice of planning.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.387
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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