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Record W4235598230 · doi:10.32920/ryerson.14647968

This is (not) a laneway. Envisioning Toronto's future mid-block communities

2021· preprint· en· W4235598230 on OpenAlexaffabout
Maya Janikowski

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan UniversityDalhousie University
FundersDivision of Graduate Education
KeywordsInfillTypologyContext (archaeology)Resource (disambiguation)Affordable housingPerspective (graphical)BusinessEnvironmental planningArchitectural engineeringCivil engineeringSociologyEngineeringGeographyComputer science

Abstract

fetched live from OpenAlex

The City of Toronto's laneway network is an untapped resource whose potential for residential development can support unique architectural opportunities and promote much needed sustainable and livable urban communities. Residential laneway development, as a form of infill, has the potential to increase the City's density without threatening the existing City fabric while providing a highly demanded housing typology. This thesis is structured around three intentions. It attempts to prove that laneway housing development is an opportunity for alleviating Toronto's housing requirements; imagines what this housing typology would look like in the context of Toronto's urban form; and explores the evolution of the laneway housing form in the entire laneway context. Arguing that when designed from this perspective, the laneway housing form has the potential to foster the growth of strong and desirable mid-block communities.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.759

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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 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

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