De-growing museum collections for new heritage futures
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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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