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Record W2605039970

Claiming the Sky; Rethinking High-Rise Development in the City of Toronto

2017· dissertation· en· W2605039970 on OpenAlexaboutno aff
Shannon Wright

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsHigh riseSkySociologyMedia studiesPolitical scienceHistoryGeographyEngineeringMeteorology
DOInot available

Abstract

fetched live from OpenAlex

Toronto is following the footsteps of populated urban cities like New York through the extrusion of skyscrapers, transforming Toronto into one of the densest cities in North America. Rapid development of residential density has produced a mono-centric core in which density is favoured over sustainable social neighbourhoods. This “gold rush” of condominium development has superseded the production of public amenity infrastructure to support the density added. Limited vacant lands, coupled with rising housing prices and the ever-increasing population, points to a potential crisis in which the long-term sustainability of these towers is questioned. Towers within the core can no longer afford to maintain the existing inflexible mono-culture, but must include public amenity infrastructure which supports the rapid density and diverse populous. The presence of the tower, soaring far beyond the ground plain, has further amplified the social and physical disconnect of the cities fabric and its inhabitants, while removing the responsibility from developers taking advantage of these trends.
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\nThis thesis aims to investigate the production of tower “neighbourhoods” through the hybridization of vertical public and private spaces. The proposal aims to question the current high rise trends and limited public amenity infrastructure within the city and provide an alternative model for porous vertical neighbourhoods in which public amenity infrastructure is used to achieve social sustainability within Toronto’s core.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.251
Teacher spread0.219 · 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 designQualitative
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
Published2017
Admission routes1
Has abstractyes

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