Bibliographic record
Abstract
Among the core technologies forming the rich archaeology of computation, cryptography is perhaps a subject that has received little attention in architectural studies thus far. However, there are fruitful considerations to draw from a closer inspection of the vast repertoire of techniques that cryptography has developed over a period of about seven centuries. First of all, a deeper historical perspective will help frame cryptography as a technology for discovery rather than solely protecting military and diplomatic secrets. Secondly, these considerations can be of relevance to design as they offer thoughts for both conceptual reflections and practical applications. At a conceptual level, the symbolic, discrete computation accompanying the evolution of cryptographic methods – such as Alberti’s one in 1467 – marked a radical departure from the iconic semiotics of analogue machines. The non-mimetic nature of symbolic computation provided the technological means to significantly widen its range of applications and enhanced speculative thinking. Understood along these lines, cryptography found more general applications beyond concealing diplomatic secrets to provide a rigorous method for inquiry into unknown domains in order to make ‘noisy data’ intelligible. Finally, symbolic computation also provided more advanced techniques for abstraction that were also instrumental for constructing notational drawings, whose emergence coincided with the introduction of more advanced mathematical instruments in the renaissance.
 The essay will discuss the key paradigmatic moments in the history of cryptography such as the polyalphabetic techniques proposed by L. B. Alberti in 1467 and the use of binary cryptography by Francis Bacon in 1605. Despite their distance from the present, these experiments provide a useful segue into a discussion on how the notion of cypher as a conceptual instrument accompanying the introduction of Machine Learning models in architectural and urban design.
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 | 0.001 |
| 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".