On An Invisible Architecture: An Antidote To Ocularcentric Space
Bibliographic record
Abstract
Invisible; something that is unseen, unnoticed, or ignored. Related to the awareness or recognition of an object as opposed to its actual presence, the problem with the invisible is the unknowingness of its very existence. While our bodies occupy diverse spatial conditions, they absorb a variety of visible and invisible information through the senses. As the geography of our increasingly digitized urban landscape creates conditions of solitude and alienation, the other senses become suppressed, manifested through our obsession with the eye, vision, and image. Identifying the non-place and its effect on inhabitants will serve as a point of departure in exploring the potentials for an architecture to become invisible — that is, for a space to exist beyond our infatuation with its aesthetic or visual character. Designing an atlas for the senses between the urban and natural context of Toronto and its ravines, this thesis aims to reintroduce architectural meaning through the body’s sensorial apparatus. In progressing beyond our current ocularcentric state, we can use the senses as perceptual mechanisms to begin a dialogue addressing the non-place-ness of urbanity. In doing so, architecture becomes more relatable, engendering new spatial, perceptual, and emotional relationships within the memory of space.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".