Bibliographic record
Abstract
Within the contemporary condition, turbulence that confronts architecture is no longer unpredictable weather patterns or wild beasts, but the unintended forces of a constantly connected digital infrastructure that demands constant attention. If, as Mark Wigley puts it, “architecture is always constructed in and against a storm” it is time for architecture to reevaluate its ability to separate us from a new storm-one that situates technology, global connectivity, human, non-human and composite users, and algorithmic architecture itself as new weather systems. Toward this end, this paper explores architecture’s ability to mediate and produce algorithmic turbulence generated through image-based sensing of the built environment. Through a close reading of Le Corbusier’s Urbanisme, we argue that for much of the 20th and the early part of the 21st century, cities have been designed to produce diagrams of smooth and homogenous flows. However, distributed personal technologies produce virtual layers that unevenly map onto the city, resulting in turbulent forces that computational platforms aim to conceal behind a visual narrative of accuracy, cohesion, anticipation, and order. By focusing on SIFT algorithms and their ability to extract n-dimensional vectors from two-dimensional images, this research explores computational workflows that mobilize turbulence towards the production of indeterminate form. These forms demarcate a new kind of challenge for both architecture and the city, whereby a cultural appetite to deploy algorithms that produce a smooth and seamless image of the world comes hand in hand with the turbulent and disruptive autonomy of those very same algorithms. By revisiting Urbanisme, a new set of architectural objectives are established that contextualize SIFTS within an urban agenda.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".