Moving Collage or «Image-temps» in Instrumental Exploration of New Modes for Analyzing, Interpreting and Conceiving Urban Spaces
Bibliographic record
Abstract
Banal space, as lived and perceived, carries the trace of time and bears the mutations that time unavoidably breeds. We are unable to perceive space in a perfectly immobile state. Everything moves: our eyes, our bodies, we in the machines that move us about, among other bodies, other machines and the natural elements that surround us. Every environment is perceived in a temporal flux – banal space is a four-dimensional space. Traditionally, architectural space is apprehended in three dimensions. It is an idealized space expressed through virtual representation. Banal space as experienced in time is difficult to master – it unfolds before us in an infinite number of unpredictable shapes and configurations and the architect, as projector and constructor, has but little control.. Ever since the beginning of cinema, moving pictures have burst into the realm of architectural thought. Architects' interest in cinema is expressed by numerous explorations conducted by modern avant-gardes, particularly in those works of the Bauhaus. Vision in Motion, written by László Moholy-Nagy, broadly disseminated these ideas in post-war architectural education. It took however nearly a century before animation was adopted into architectural representation as a common instrument to think, analyse, conceive and create space. Everything seemed to evolve significantly with the increasing flexibility of digital tools, however in the process, something had been lost. The space-time generated by 3D software programs commonly used by architects had neither the quality nor richness of initial explorations with conventional film. This long trajectory of architectural representation from static to moving image involves perhaps another component that embodies obstacles of a nature other than technical.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".