Bibliographic record
Abstract
Museums find themselves in tension. In order to collect and conserve artefacts representative of their subject, they have needed to be physically stable places. But to remain useful organs of the present, they cannot extract in the manner that was common to many of them, with varying degrees of invasion and imposition, for so long. The question: how can a museum fulfill its mnemonic, protective, and presentational roles in a world in which it is interested, but in a way that steps more lightly in that world? The Canadian Centre for Architecture is under this kind of tension, and deriving energy from it. Our collection, which has grown by dialogue and donation, and which is conserved, exhibited, and accessible for research in a building in Montreal, is an essential aspect of our existence; and yet we are evolving into a more mobile and observational entity. We have been experimenting with models of research, conservation, and curation that, for their emphasis on non-invasively processing phenomena in their existing context, are called “post-custodial”. These interrelated models, including an archival process focussed on threatened archives called Find and Tell Elsewhere and a medium-term curatorial process focussed on different cities around the world called CCA c/o, provide in-situ support to endemic architectural voices and discourses. Collaborations in regions in Africa, including Senegal, Sudan, and Nigeria, have been formative in shaping these models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.121 | 0.020 |
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".