Taking Stock of "Taking Stock: Museum Studies and Museum Practices in Canada"
Bibliographic record
Abstract
Taking Stock Steering Committee !In April 2010, the Museum Studies program celebrated its 40-year history as a graduate program at the University of Toronto -it is the country's oldest museum studies program -by organizing an academic conference dedicated to museology.With the explicit aim of creating a forum for discussing the historical and contemporary trajectories of museological research and practices in Canada, Taking Stock: Museum Studies and Museum Practices in Canada drew together an English and French-speaking community of approximately 150 scholars, museum professionals, and graduate students from across Canada, the U.S. and the U.K. The three-day conference addressed a number of contemporary themes ranging from curation and exhibition pedagogy to First Nations museology, museum management, and civics and sustainability. !Museology is characterized by a double encoding of interdisciplinarity: on one level, the field is informed both by professional and academic training, and in a second instance, the field involves the interaction of several academic disciplines in its very formulation.For these reasons, museological research is often disseminated across the humanities and social sciences to subject area specializations such as anthropology, art history, history, material and visual culture and science in ways that may or may not reach the researchers who identify themselves as museologists.Taking Stock brought together a number of these scholars and practitioners, who presented on panels that deliberately sought to cut across disciplinarian differences to establish common ground in issues-based dialogue. !This special issue of Faculty of Information Quarterly takes stock of some of the outstanding presentations provided by participants at the Museum Studies' conference last April.The five articles published in this issue reveal the richness and diversity of concepts and ideas explored by researchers and professionals in a field that is increasingly active amidst the many civic institutions and resources activating for global change. !Valentine Moreno and Tabitha Minns consider a range of political and cultural implications surrounding the international contexts of art curatorship, exhibition practices and museology.John Rubino and co-authors Jennifer Forsyth and Jessica Leavens examine contemporary programming strategies and practices created for diverse adult publics and museum practitioners in Canadian art galleries, museums, and provincial museums associations.Informed by an interest in lifelong learning, Viviane Gosselin probes notions of identity and meaning making by examining both the production and reception of two exhibitions at two Canadian museums.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 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".