Bibliographic record
Abstract
In this paper I investigate how different modes of urban transportation shape our experience of the urban environment. My goal is to argue that how we move through a space is not merely a question of convenience or efficiency. Rather, our transportation technologies can fundamentally shift how we experience where we are. I propose a framework for considering mobility from the standpoint of phenomenological everyday aesthetics considering the social, somatic, temporal-epistemic, and affective characteristics of experience. I then suggest a typology of different forms of urban mobility distinguishing between private and public forms of transportation as well as between faster and slower modes. I next suggest a trio of factors—speed, ability to survey one’s surroundings, and ease of interruption—that play into how we experience an urban environment while discovering it by means of mobility. By applying the framework of experience and the trio of factors to the typology of transportation modes I show how each of them can foster or hinder an aesthetic experience of the urban environment. I conclude by reflecting on some further issues for investigation including the role of power in urban space, questions concerning mobility and difference (class, race, dis/ability, etc.), the place of technological mediation in urban mobility, and the role of spatial planning.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".