Bibliographic record
Abstract
One of the most controversial aspects of museum governance has been the practice of deaccessioning, whereby museums sell or otherwise part with possession of objects forming part of their collections. Though such transfers are usually legal, they can sometimes engender heated debate. Much of the controversy surrounding deaccessioning by museums seems to arise from the perception that they are public institutions impressed with the role of protecting and preserving their collections intact for future generations.However, the enormous market prices for certain works of art in recent years have created tempting options for museums to raise funds by selling objects from their collections. This article aims at illustrating the problems institutions face when considering the disposition of objects in their collections. The legal framework that governs such sales will be outlined and the question posed as to whether these existing laws are adequate to address concerns surrounding sales, or whether new laws or other strategies are needed to resolve contentious dispositions.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.080 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".