Book Reviews
Bibliographic record
Abstract
Pál Nyíri, Mobility and Cultural Authority in Contemporary China (Jason Lim) Friedrich von Borries, ed., Berliner Atlas paradoxaler Mobilität (Anne-Katrin Ebert) Toni Morrison, Home (Jennifer Reut) Antonio Amado, Voiture Minimum, Le Corbusier and the Automobile (Ernie Mellegers) Kurt Stenross, Madurese Seafarers. Prahus, Timber and Illegality on the Margins of the Indonesian State (Malcolm Tull) Gordon Pirie, Cultures and Caricatures of British Imperial Aviation: Passengers, Pilots, Publicity (Liz Millward) Christine R. Yano, Airborne Dreams: “Nisei“ Stewardesses and Pan American World Airways (Stéphanie Ponsavady) Christophe Gay, Vincent Kaufmann, Sylvie Landriève, Stéphanie Vincent-Geslin, eds., Mobile/Immobile: Quels choix, quels droits pour 2030/Choices and Rights for 2030 (Patricia Lejoux) Zhang Ellen Cong, Transformative Journeys: Travel and Culture in Song China (Nanny Kim) Susan Sessions Rugh, Are We There Yet? The Golden Age of American Family Vacations (William Philpott) Justin D. Edwards and Rune Graulund, Mobility at Large: Globalization, Textuality and Innovative Travel Writing (Steven D. Spalding)
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.405 | 0.316 |
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".