Bibliographic record
Abstract
Contemporary debates in architectural criticism often turn on the identification of a building's proper function.Thus a key step in resolving such debates would be to understand how buildings come to have their proper functions.In this essay, I argue that buildings, like other artefacts, obtain their proper functions, not in virtue of architects' intentions, but in virtue of their histories of selection in the marketplace.I show how this theory of architectural function can advance critical debates by discussing the case of Libeskind's controversial addition to Toronto's Royal Ontario Museum. Fact and Function in Architectural Criticism1. What are we building here?According to Vitruvius, successful works of architecture are supposed to possess three virtues: durability, convenience and beauty. 1 Of these, one might expect the last, beauty, to be the most difficult to apply, and so to be the focus of disputes in architectural criticism.Beauty, after all, is widely believed to be "in the eye of the beholder," and many different judgments about whether something is beautiful cannot be resolved through empirical study or rational debate.Whether something is durable, in contrast, seems to be clearly a matter of fact; something that can be assessed empirically, for example, by measuring the ability of a structure to withstand stress or not to deteriorate when exposed to the elements.Convenience-which today we might call "utility"-is more complex, but seems similarly based in fact.For example, to assess the utility of a hospital we could measure the speed with which personnel and supplies may be moved through it.For these reasons, we might well expect beauty to be the real sticking point of disputes within architectural criticism.However, many interesting cases in contemporary architectural criticism seem not to meet this expectation.Here it is the issue of utility, or functionality, that has emerged as the nub of some important disagreements, though not in quite the way one might expect. 2Consider, as an example, Daniel Libeskind's 2007 extension of Toronto's Royal Ontario Museum (ROM).Libeskind's construction, now known (after one of its philanthropic patrons) as the Lee-Chin Crystal, was a renovation of the entrance to the previously existing museum complex.A dramatic structure composed of sloping walls that meet at sharp angles, the Crystal appears to
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".