Automobility realism: How the auto-dominated present constrains our imagined futures
Bibliographic record
Abstract
In the twentieth century, the buildout of an auto-oriented transportation system fundamentally altered the social and economic systems of Western countries.The new system of mobility fuelled unsustainable land uses, environmental destruction, and an increase in transportationrelated deaths and injuries which disproportionately affect low-income, racialized, elderly, young, and other vulnerable groups.As such, visions for a future of transportation which address these problems are urgently needed.Some of the most widely popularized ideas are those promoted by executives in the technology industry, but there has been little critical analysis of whether these ideas will actually address the harms and inequities of the existing system.Using a mix of interviews, corporate documents, and conceptual images, along with books, peerreviewed research, independent studies, and journalism, I interrogate the claims made by leaders in the technology industry about the prospects of electric vehicles, ride-hailing services, autonomous vehicles, flying cars, and a series of tunnels for cars; and compare them to the actual impacts of those solutions that have already been implemented, and the likely impacts for those which remain theoretical.I argue that the system of automobility has constrained people's ability to imagine an alternative to an auto-dominated transportation system, which I term 'automobility realism', and that the ideas presented by tech executives fail to truly address the harms and inequities of the existing transportation system.Rather, the integration of technologies allows for narrow benefits which primarily accrue to well-off individuals, while potentially creating new harms for vulnerable groups.I conclude that the problems of automobility will only be solved when people are empowered to imagine futures beyond the dominance of automobiles in urban space.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".