Bibliographic record
Abstract
The thesis reflects on the loss of slowness and experiential depth in the age of acceleration. The “contrived depthlessness” of modernity – as described by Frederic Jameson– can be traced back to contemporary culture’s fixation with appearances, surfaces, and instant impacts. Modern architecture favors enticing forms and transparency at the price of tactility and the slow unfolding of spaces. Based on the author’s personal experience of tactile spaces within the city, and a reflection on the existing body of literature on places of stillness, the thesis identifies landscape engagement and layered threshold as the main approaches to generating embodied depth within the city. \n\tSt. James Town in Toronto, a cluster of post-war tower block that serves as a gateway community for many newcomers, is marked by an extreme spatial flatness and anonymity. Harsh delineation of private and public realm hinders inhabitants’ connection with the city and fellow city-dwellers. St. James Town’s notable flatness is rooted in the 1920’s urban planning vision of Towers-in-the-Park, which is described by Le Corbusier as “a city made for speed”. For this reason, it is through the implementation of meaningful social interaction and landscape engagement on this site that the potential for architecture to generate embodied depth can be evaluated. \n\tBased on the fundamental value of a deep embodied space as a catalyst of memory and spatial appropriation, the thesis proposes a series of landscape and spatial layers at multiple scales including: an urban block, individual towers, and pocket gardens. The resulting diversity of paths and social programs encourage the inhabitants’ participation and appropriation of the cityscape. The thesis deems that the ethical role of architecture in the age of acceleration is to restore the natural slowness of experience and strengthen our sense of the real.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".