Book Reviews
Bibliographic record
Abstract
Women Mean Business: Colonial businesswomen in New Zealand, Catherine Bishop. Dunedin: Otago University Press, 2019. Imagining Decolonisation, Rebecca Kiddle with Bianca Elkington, Moana Jackson, Ocean Ripeka Mercier, Mike Ross, Jennie Smeaton, and Amanda Thomas, eds. Wellington: Bridget Williams Books, 2020. Cosmopolitan Ambassadors: International Exhibitions, Cultural Diplomacy and the Polycentral Museum, Lee Davidson and Leticia Pérez Castellanos. Wilmington, DE: Vernon Press, 2019. Museums, International Exhibitions and China’s Cultural Diplomacy, Linda Da Kong. London: Routledge, 2021. Curating (Post-)Socialist Environments, Philipp Schorch and Daniel Habit. Bielefeld: Transcript Verlag, 2021. A Cultural Arsenal for Democracy: The World War II Work of US Museums, Clarissa J. Ceglio. Boston: University of Massachusetts Press, 2022. Mobile Museums: Collections in Circulation, Felix Driver, Mark Nesbitt, and Caroline Cornish, eds. London: UCL Press, 2021. Écrire la muséologie: Méthodes de recherche, rédaction, communication [Writing museology, Research methods, writing, communication], François Mairesse and Fabien Van Geert. Paris: Presses Sorbonne Nouvelle Ed, 2021. Cultural Renewal in Cambodia: Academic Activism in the Neoliberal Era, Philippe Peycam. Leiden: Brill, 2020. Animal Classification in Central China: From the late Neolithic to the early Bronze Age, Ningning Dong. Oxford: BAR Publishing, 2021.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.556 | 0.485 |
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".