Giants in the lab: Model conservation and the anaphoric progression of design
Bibliographic record
Abstract
How is an architectural model consolidated and re-assembled in conservation to be able to continue to communicate a design concept? How does the work of care and preservation of models reveal knowledge about the often taken-for-granted dynamics of creative processes? To provide answers, this article draws on Etienne Souriau’s philosophy of creativity and follows how the ‘modes of existence’ of creative works are re-enacted in the anaphoric progression of conservation. Basing her findings on ethnography at the Canadian Centre for Architecture, the author examines the epistemic complexity of specific situations of assessing, preserving and assembling large complex scale models. Unpacking the specificity of model conservation, it is argued, allows us to challenge two established beliefs on creativity: the myth of the stable ontology of historically valuable cultural objects and the myth of teleology of creative processes. Conservation-in-action demonstrates the subtle mechanics of crafting historiographic knowledge in the arts.
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".