Bibliographic record
Abstract
Contents: Phillip Vannini/Lucy Budd/Ole B. Jensen/Christian Fisker/Paola Jiron: Technologies of Mobility in the Americas: Introduction - Mimi Sheller: Virtual Caribbeans: Edens, Economies, Elsewheres - Rob Shields: Knowing Flows: How Migration Research Meets Mobilities Through Digital Technology - Ole B. Jensen: If Only It Could Speak: Narrative Explorations of Mobility and Place in Seattle - Nick Scott: How Car Drivers Took the Streets: Critical Planning Moments of Automobility - Lucy Budd: Selling the World: Airline Advertisements and the Promotion of International Aeromobility in National Geographic, 1964-2004 - Jennie Germann Molz: Solidarity on the Move: Technology, Mobility, and Activism in a Hospitality Exchange Network - Christian E. Fisker: Glimpses of Motility of the Networked Self Across the Life Course - Kim Sawchuk/Barbara Crow: Seniors, Cell Phones, and Tactical Restriction - Phillip Vannini/Rhys Evans: Haunting Technologies: Performing Memories of Place Through Effervescent Mobilities - Tamara Shepherd/Leslie Regan Shade: Mobile Phones as a Necessary Evil: Canadian Youth Talk About Negotiating the Politics of Mobility - Jim Conley: A Sociology of Traffic: Driving, Cycling, Walking - Noel B. Salazar: Imaginative Technologies of (Im)mobility at the End of the World - Paola Jiron: Technology and Technicians Out of Control: The Implementation of Transantiago From a Daily Mobility Point of View.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".