Collections Management as Critical Museum Practice
Bibliographic record
Abstract
There is a common misconception that collections management in museums is a set of rote procedures or technical practices that follow universal standards of best practice. This volume recognises collections management as a political, critical and social project, involving considerable intellectual labour that often goes unacknowledged within institutions and in the fields of museum and heritage studies. Collections Management as Critical Museum Practice brings into focus the knowledges, value systems, ethics and workplace pragmatics that are foundational for this work. Rather than engaging solely with cultural modifications, such as Indigenous care practices, the book presents local knowledge of place and material which is relevant to how collections are managed and cared for worldwide. Through discussion of varied collection types, management activities and professional roles, contributors develop a contextualised reflexive practice for how core collections management standards are conceptualised, negotiated and enacted. Chapters span national museums in Brazil and Uganda to community-led heritage work in Malaysia and Canada; they explore complexities of numbering, digitisation and description alongside the realities of climate change, global pandemics and natural disasters. The book offers a new definition of collections management, travelling from what is done to care for collections, to what is done to care for collections and their users. Rather than ‘use’ being an end goal, it emerges as a starting point to rethink collections work.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".