Walter Gropius’s silos and Reyner Banham’s grain elevators as art-objects
Bibliographic record
Abstract
In Reyner Banham’s slide collection are numerous photographs of his visits during the 1970s and 1980s to grain elevators in the USA and Canada. They are archived and catalogued alongside his own photographs of others from the early twentieth century, including the image Walter Gropius used to first introduce the now iconic North and South American silos into architectural discourse in his 1911 lecture ‘Monumentale Kunst und Industriebau’ (Monumental Art and Industrial Building). Together these images provide an account through photography of Banham’s return to these canonical buildings (Figure 3.1). They allow us to question Gropius’s and Banham’s deliberate choice of photographic representations, and to explore their (apparently) different use of photography as a vehicle to introduce ordinary and vernacular industrial buildings as art-objects worthy of architectural attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".