Bibliographic record
Abstract
This article explores how e-micromobility (EMM) can produce 'good' active travel together with cycling. Foregrounding the unique affordances of the electric unicycle (EUC) and e-bike, we highlight their potential to produce good active travel with cycling through protected bike lanes and traffic-calmed neighbourhood greenways. We argue that electric unicycling and e-biking can create good active travel together with cycling by advancing multiple and competing visions of the common good or political philosophies of mutual flourishing. We imagine 'good' active travel as practices and infrastructures that equip a plurality of commons goods, based on industrial, market, civic, domestic and ecological worths, as well as challenge the hegemonies of automobility and market worth. Using mobile ethnographic data from Vancouver, our analysis shows that electric unicycling and e-biking can, through infrastructures shared with cycling, advance these common goods, including decolonizing extensions of domestic worth, while challenging the car and neoliberal capitalism. Ultimately, we conclude that 'good' futures for electric unicycling, e-biking and cycling demand mobility justice through a consolidation of their decolonizing, civi and ecological worths at the expense of their industrial and market worths.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".