Bibliographic record
Abstract
Despite the explosive growth of mobilities research, much sociospatial theory continues to be rooted in a sedentarist perspective, failing to incorporate the insights of this burgeoning field. Mobilities research, in contrast, often considers a variety of sociospatial relations, yet stops short of coherent integration with other dimensions of sociospatiality. In this article, we examine the mobilities turn in light of Jessop, Brenner, and Jones's (2008) TPSN framework, which recognizes the polymorphic nature of sociospatial relations. We discuss the interrelationships between mobility and the four distinct sociospatialities identified by Jessop, Brenner, and Jones: territory (T), place (P), scale (S), and networks (N). Each of these sociospatialities is coimplicated with mobility: Territory concerns the malleable areal and bordered structure of the state and the uneven freedoms granted, and constraints imposed on, objects and bodies as they attempt to move through and across political jurisdictions; place emphasizes the embedded and performative nature of mobility and considers place-appropriate and place-transgressive activity; scale concerns movement associated with the tangled and politicized processes of scale production and examines how mobility is affected by the uneven scaling of power, resources, opportunity, and identity; networks address flows of bodies, objects, and knowledge across space, through specific channels. To illustrate the coimplicated relationships among mobility and territory, place, scale, and networks, we examine the practice of automobility, stressing the ontological contingency of mobility: Neither mobility nor fixity can be assumed. Mobility is, rather, a social, cultural, and political achievement, inherently power-laden and recursively bound up in the production of territory, place, scale, and networks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".