Book Reviews
Bibliographic record
Abstract
The Future of Digital Data, Heritage and Curation in a More-than-Human World Fiona R. Cameron. Abingdon: Routledge, 2021. What Photographs Do: The Making and Remaking of Museum Cultures Elizabeth Edwards and Ella Ravilious, eds. London: UCL Press, 2022. The Aftermaths of Participation: Outcomes and Consequences of Participatory Work with Forced Migrants in Museums. Susanne Boersma. Bielefeld: Transcript Verlag, 2023. Museum Times: Changing Histories in South Africa Leslie Witz. New York: Berghahn Books, 2022. Ancestors, Artefacts, Empire: Indigenous Australia in British and Irish Museums Gaye Sculthorpe, Maria Nugent, and Howard Morphy, eds. London and Canberra: The British Museum Press and the National Museum of Australia, 2021. The Routledge Companion to Indigenous Art Histories in the United States and Canada Heather Igloliorte and Carla Taunton, eds. New York: Routledge, 2022. Becoming Our Future: Global Indigenous Curatorial Practice Julie Nagam, Megan Tamati-Quennell, and Carly Lane, eds. Winnipeg: Arbeiter Ring Publishing, 2020. History Making a Difference: New Approaches from Aotearoa Katie Pickles, Lyndon Fraser, Marguerite Hill, Sarah Murray, and Greg Ryan, eds. Newcastle upon Tyne: Cambridge Scholars Publishing, 2017.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.438 | 0.332 |
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".