MétaCan
Menu
Back to cohort
Record W4232584319 · doi:10.32920/ryerson.14652192

Sociotope mapping: perceptions of public space at Ryerson University

2021· preprint· en· W4232584319 on OpenAlexaffabout
Olivia Afonso Magalhaes

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPublic spaceRealmContext (archaeology)Space (punctuation)PerceptionArchitecturePublic participationPublic relationsSociologyGeographyKnowledge managementComputer sciencePolitical scienceArchitectural engineeringEngineeringPsychology

Abstract

fetched live from OpenAlex

Sociotope mapping is a tool that has been used to identify values in public spaces, as defined by the public. By developing an original sociotope map using the sociotope map methodology, utilizing the technique created in Stockhom, Sweden, this research attempts to understand the values of public space within and around Ryerson University, while providing a critique on the utility of the tool in this context. The information collected from an online survey will be analyzed and visually displayed on a sociotope map. This may be utilized by the school administration, municipal planners, urban designers or landscape architecture professionals to understand what concerns may be provoked by the development of certain spaces and the resources valued by the public in the public realm. This project explores how different public spaces within the Ryerson University Campus are utilized and how useful is the sociotope mapping tool in inferring these values. keywords: planning; sociotope; parks planning; perceptions of space; engagement; public consultation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.188
Teacher spread0.160 · 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.

Study designSimulation or modeling
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

Explore more

Same topicUrban Design and Spatial AnalysisFrench-language works237,207