Putting the car in context: a call for a situated technopolitical transition in global automobilities
Bibliographic record
Abstract
With more than half of automobile sales for 2025 centered upon Global South countries, examining the embedding of global automobilization in these contexts and the potential to transition to greener modes is vitally important but largely unaccomplished. Further, despite the deep embedding of automobilization, sociotechnical approaches have proposed constitutively apolitical pathways for a sustainable transition. In this agenda article, we outline a theory and practice of situated technopolitical transition to respond to the power-filled automobilities being implanted in global Southern cities. Our theorization begins from the assertion that transition studies is not universal, as often presupposed, but rather highly contextual and emerging from a specific experience of colonial modernity. Drawing upon ethnographic research in Indian cities complemented with relational comparison with other contexts, we sample a range of situational politics that surround efforts to manage automobilization. We assert that, the technopolitics of global automobilities include phenomena such as the worlding of globalizing cities; kinetic elite claims to street space; and the invisibilization of women and others. We conclude by outlining the practice of an alternate techno-political transition strategy that is rooted in a Southern politics of radical incrementalism.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".