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Record W2310882819 · doi:10.25071/ryr.v1i0.40301

Towers of Power: An Empirical Analysis of Toronto’s Central Business District

2014· article· en· W2310882819 on OpenAlexaboutno aff
Jonathan Kitchen

Bibliographic record

VenueYork University Digital Library (York University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHegemonyCentral business districtPower (physics)SociologyPoliticsMiddle classPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

Over the past sixty years, Toronto’s Central Business District (CBD) has become home to an ever-increasing densification of corporate office towers. Accessibility, control, power, and securitization of property has developed through the post-industrial evolution of the built environment, resulting in the exclusion of some individuals. This paper, through theoretical analysis and empirical data collection, argues that unequal access to privatized public spaces within the CBD’s office towers, outdoor plazas, and the underground PATH system reinforces class distinction and capitalist hegemony. The qualitative empirical data used within this paper were collected through observational site visits of twelve corporate office towers and plazas in Toronto’s CBD, as well as the PATH system. Through the analysis of these spaces, this paper concludes that the built environment of the CBD serves only the needs of the dominant capitalist and middle/upper-middle classes. The class distinctions and capitalist hegemony written into these built environments are reinforced through social, cultural, and physical controls of space. On a larger scale, the spatial exclusion of marginalized individuals within the urban environment speaks to a greater social and spatial inequality within the city, suggesting the need to re-evaluate systems of social, economic, and political power.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.003
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 head0.011
GPT teacher head0.201
Teacher spread0.189 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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