Docklessness, aesthetic governance, and the urban ‘micromobility mess’
Bibliographic record
Abstract
Dockless micromobility sharing systems have wrought significant visual changes to urban streetscapes worldwide. These changes are often described in terms of the ‘mess’ of micromobility, characterised by dockless vehicles abandoned in roadways, sidewalks, and recreational paths, tossed into waterways, and graffitied, burned, and otherwise vandalised. In this paper, we argue that efforts to govern this dockless micromobility mess – which most frequently comes in the form of parking regulations – effectively impose and enforce normative visual order on the cityscape. Based on an analysis of primary image data and publicly available documents, we identify that efforts at governing docklessness also have the effect of governing the aesthetics of urban space in three ways: through (1) visual-material interventions (e.g. parking corrals and mats, app interfaces); (2) linked strategies of visual verification (digital image capture and assessment) and computer vision (the use of AI and machine learning); and (3) visual erasure (e.g. impounds and bikeshare graveyards). We discuss the implications of the aesthetic effects of micromobility governance for docklessness itself and the utilisation of dockless micromobilities and potential impacts on transportation equity and sustainability, and most significantly, for the right to the city.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".