A History of Participation in Museums and Archives
Bibliographic record
Abstract
Traversing disciplines, A History of Participation in Museums and Archives provides a framework for understanding how participatory modes in natural, cultural, and scientific heritage institutions intersect with practices in citizen science and citizen humanities. Drawing on perspectives in cultural history, science and technology studies, and media and communication theory, the book explores how museums and archives make science and cultural heritage relevant to people’s everyday lives, while soliciting their assistance and participation in research and citizen projects. More specifically, the book critically examines how different forms of engagement are constructed, how concepts of democratization are framed and enacted, and how epistemic practices in science and the humanities are transformed through socio-technological infrastructures. Tracking these central themes across disciplines and research from Europe, Canada, Australia and the United States, the book simultaneously considers their relevance for museum and heritage studies. A History of Participation in Museums and Archives should be essential reading for a broad academic audience, including scholars and students in museum and heritage studies, digital humanities, and the public communication of science and technology. It should also be of great interest to museum professionals working to foster public engagement through collaboration with networks and local community groups.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".