Towers of Power: An Empirical Analysis of Toronto’s Central Business District
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".