Speed as an expression and texture of space: Theory at play in a movement activity
Bibliographic record
Abstract
In recent years, following new materialist, posthumanist and non-representational turns, human geography has increasingly understood the worlds it studies as vital, immediate and emergent. As part of this vision, studies have empirically animated and theoretically articulated various expressions/textures in the movement of space, including its rhythms, shapes, timings, repetitions, sensuousness, and infections. Speed is one such expression/texture that has received some empirical attention but, in comparison to most others, has not been so thoroughly theorized. In response, this paper conducts a reconnaissance into speed, its intentions being to convey some foundational theoretical understandings of speed and, through empirical research, show these at play in social contexts. Specifically, naturalistic participant observations of forms of the movement activity of cycling are used to animate how; (i) speed can be represented and affective as a scalar quantity; (ii) all objects possess speeds and affect other speeds; (iii) speeds and objects are known through relative positions and speeds; (iv) speeds create rates of happening; (v) speeds occur in all expressions/textures of space; (vi) the accelerating world is engaged at relational speeds. From this reconnaissance, to assist future research on speed, the paper closes with some suggested avenues for further inquiry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| 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".