Bibliographic record
Abstract
Despite their prominence in everyday life lineups are of peripheral concern to mobility scholars. Aiming to contribute to our existing knowledge on lineups and the transitory places of everyday life writ large, this paper attempts investigates lineups at small island ferry terminals. Drawing upon fieldwork including travel to, on and from ferry boats, for a total of about 250 journeys over three years, and about 400 qualitative interviews, this mobile ethnography focuses on practices of ferry mobility in coastal British Columbia. Lineups are portrayed as complex orchestrations of rest and movement weaved through relational performances of mobility and relative immobility. As neither a place in the sedentarist nor nomadic sense, lineups defeat facile, dichotomous conceptualizations of spatialities and temporalities. Neither still nor flowing, neither public nor private, lineups are animated by idiosyncratic practices of dwelling whereby multiple and unique forms of livelihood are performed. Ferry lineups are ephemeral moorings: places where communities form and dissolve in temporary zones, as if suspended from the regular rhythms of the rest of the day and the week. On small islands lineups exist as stolen time‐spaces – an original concept that draws inspiration from the musical idea of tempo rubato and from Michel de Certeau’s (1984) treatment of tactics.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".