Conservation : principles, dilemmas and uncomfortable truths
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Jonathan Ashley-Smith, formerly V&A Museum Erica Avrami, World Monuments Fund Zuzana Bauerova, Cultureplus Ltd Isabelle Brajer, National Museum of Denmark Simon Cane, Birmingham Museums and Art Gallery Chris Caple, University of Durham Miriam Clavir, Museum of Anthropology at the University of British Columbia Helen Clifford, University of Warwick Dinah Eastop, Textile Conservation Centre, University of Southampton Jim Enote, A:shiwi A:wan Museum and Heritage Center / Mountain Institute Tina Fiske, University of Glasgow Cathleen Hoeniger, Queen's University Canada Jukka Jokilehto, formerly ICCROM and ICOMOS Marian A. Kaminitz, National Museum of the American Indian Jonathan Kemp, V&A Museum Harvey Molotch, New York University Salvador Munoz Vinas, Universidad Politecnica de Valencia Elizabeth Pye, University College London Curtis Quam, A:shiwi A:wan Museum and Heritage Center Jonathan Ree, freelance philosopher and historian Marcelle Scott, University of Melbourne Catherine Smith, University of Otago Nicholas Stanley-Price, formerly ICCROM Jill Sterrett, San Francisco Museum of Modern Art W. Richard West, Jr., Founding Director Emeritus, National Museum of the American Indian Glenn Wharton, Museum of Modern Art Eileen Yatsattie, Potter
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.
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 | 0.000 |
| 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) | 0.007 | 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 it