Questioning models and drawings Representing space in drawing, film and writing
Bibliographic record
Abstract
This double issue of arq includes a number of papers first presented at a conference held in Nottingham in November 2005 which was hosted jointly by AHRA (the Architectural Humanities Research Association) and the School of the Built Environment at the University of Nottingham, in conjunction with the Nottingham-based Image Studies Network. The theme for the event was set by Professor Marco Frascari, Director of the School of Architecture at Carleton University, Ottawa, and also a Leverhulme Visiting Professor at the University of Nottingham in 2005–06. Both ‘models’ and ‘drawings’ have been interpreted in a particular way by Frascari, who wrote in the event’s Call for Papers: ‘Nowadays, we know what kinds of drawings architects make. They have been codified by tradition, by profession and by legislation. Although this canonisation is a relatively recent event nevertheless it has reached a condition where innovation is almost impossible. The architect’s drawings have become “models” and generate “models” to be preserved in museums, magazines and archives. To challenge this idle condition it is necessary to question the imagination of construction and the construction of imagination and how these processes affect and effect the envisioning of architecture in absentia’. The conference thus addressed relationships between drawings and buildings around four key themes: the tendency of architectural representations to become ‘models’ for imitation, following Frascari’s interpretation of that word; the claim of new imaging technologies to make visible what could be described as the previously unseen; the cognitive spatial implications of traditional imaging practices relative to CAD; and the critical potential of the architectural image.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".