Bibliographic record
Abstract
Winner of the 2018 Ontario Museum Association Award of Excellence Winner of the 2019 Canadian Museum Association Award of Outstanding Achievement in the Research - Cultural Heritage Category Creating Exhibits that Engage: A Manual for Museums and Historical Organizations is a concise, useful guide to developing effective and memorable museum exhibits. The book is full of information, guidelines, tips, and concrete examples drawn from the author’s years of experience as a curator and exhibit developer in the United States and Canada. Is this your first exhibit project? You will find step-by-step instructions, useful advice and plenty of examples. Are you a small museum or local historical society looking to improve your exhibits? This book will take you through how to define your audience, develop a big idea, write the text, manage the budget, design the graphics, arrange the gallery, select artifacts, and fabricate, install and evaluate the exhibit. Are you a museum studies student wanting to learn about the theory and practice of exhibit development? This book combines both and includes references to works by noted authors in the field. Written in a clear and accessible style, Creating Exhibits that Engage offers checklists of key points at the end of each chapter, a glossary of specialized terms, and photographs, drawings and charts illustrating key concepts and techniques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".