De-growing museum collections for new heritage futures
Bibliographic record
Abstract
This article focuses on curators’ frustrations with (what we call) ‘the profusion struggle’. Curators express the difficulty of collecting the material culture of everyday life when faced with vast existing collections. They explain that these were assembled, partly, from anxiety to gather up what was anticipated at risk of being lost. Unlimited accumulation, and keeping everything forever, are being called into question, especially through the disposal debate which has gained in intensity over the past three decades. While often with some reluctance, setting limits by slowing collecting or even reducing collections through targeted letting go, or what is variously called ‘deaccessioning’, ‘disposing’, and ‘refining’ collections, are undertaken to facilitate ongoing collecting, amongst other goals. To respond to curatorial interest in strategies for addressing profusion, we draw on ethnographic fieldwork looking predominantly at social history museums in the United Kingdom, to consider whether ideas borrowed from beyond museums might be of use. We explore the possible implications of economic concepts of ‘de-growth’ – partly by seeing the ways that these ideas are already practiced, but also by examining curators’ own enthusiasms and reservations. To develop more sustainable collecting practices, we argue that ideas of collections ‘growth’ might be usefully reframed.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".