Bibliographic record
Abstract
What constitutes an archive in architecture? What forms does it take? What epistemology does it perform? What kind of craft is archiving? Crafting History provides answers and offers insights on the ontological granularity of the archive, and its relationship with architecture as a complex enterprise that starts and ends much beyond the act of building, or the life of a creator.We learn how objects are processed and catalogued, how a classification scheme is produced, how models and drawings are preserved, how born-digital material battles time and technology obsolescence. We capture archiving in its mundane, and practical course. We follow the work of conservators, librarians, cataloguers, digital archivists, museum technicians, curators, and architects.Based on ethnographic observation of the Canadian Centre for Architecture and interviews with a range of practitioners, including Álvaro Siza and Peter Eisenman, Yaneva traces archiving through the daily work and care of all its participants, scrutinizing their variable ontology, scale, and politics. Yaneva addresses the strategies employed by practicing architects to envisage an archive-based future, and tells a story about how architectural collections are crafted so as to form the epistemological basis of Architectural History
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".