Bibliographic record
Abstract
I set out to learn something basic about architecture, something foundational on which to situate the conceptual and rhetorical exercises played within the studio. In settings both academic and professional I had been encouraged to reduce my study of architecture to a cerebral and retinal game of sorts played out via ever-increasingly seductive imagery. It seemed apparent that in order to think about architecture I should have been involved in an act of architecture. My intentions, albeit naïve, were to engage architecture on its own terms, through its own medium, to return to first principles, if there ever were any, and to acquire a form of embodied architectural knowledge inseparable from its material becoming. There was no amount of hypothesizing, theorizing, no amount of digital sophistication that could supplant the basic educational experience gained from involving myself with real materials, in a real place, with a fully engaged being. With this in mind I journeyed into Ontario’s North, with little more than a hammer and saw and a desire for experience, that most brutal of teachers. I would engage in a basic act of building as a method of acquiring a deeper understanding of the subject I had been studying for several years yet whose essence I felt I knew very little about. The resultant document, informed by traditions of the primitive hut, records a journey towards architectural embodiment; it resides as an argument for the reintroduction of embodied forms of learning into the education of the architect.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.338 | 0.077 |
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".