Bibliographic record
Abstract
The Manual of Digital Museum Planning is a comprehensive guide to digital planning, development, and operations for museum professionals and students of museums studies and arts administration. In the tradition of Lord Cultural Resource’s renowned manuals, this book gives practical advice on how digital can enhance and improve all aspects of the museum. With chapters written by experienced professionals working at leading institutions such as the British Museum, the Metropolitan Museum of Art, the Indianapolis Museum of Art, Bristol Culture, the Canadian Museum for Human Rights, and others, The Manual of Digital Museum Planning is an easy-to-understand, step-by-step guide for anyone planning a new museum, a museum expansion, or a new project in the Digital Age. Part 1 explains how digital technologies are transforming museums and their value proposition Part 2 explores how adopting a user-centric, omnichannel approach creates new relationships between museums and communities Part 3 offers a guide to integrating digital into the workflow of museums- from data analytics, to user experience design to project management Part 4 identifies the business models, infrastructure and skills and competencies for the digital museum, Each chapter culminates in ‘summary takeaways’ for easy recall, and key words are defined throughout. A glossary and reference list are also included as an accessible resources for readers.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.266 | 0.174 |
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".