Automobility, velomobility, and complete streets? Discursively analyzing the politics of complete street design guidelines in Ontario, Canada
Bibliographic record
Abstract
Complete streets are a widely used planning concept that decenters cars and provides roadway and boulevard space to pedestrians, cyclists, and transit; often with the objective of accommodating road users of ‘all ages and abilities’. However, the inclusivity of complete streets has been recently called into question due to a disconnect between what is promised and what is realized. We argue that to understand this gap, the politics of mobility—including the ideologies, norms, and values—produced and reproduced within complete streets planning processes must first be understood. We conducted a critical discourse analysis of five complete streets design guidelines from Ontario, Canada, examining the plans for their relationality to automobility and velomobility, including the mobilities supported, places produced, and types of bodies and identities considered. In this, we found that complete streets reproduce ideologies of automobility through positioning sustainable transportation modes and the public space of streets as efficient and economically productive. Such entanglements to automobility can limit the potential of complete streets to meaningfully accommodate ‘all ages of abilities’. These findings suggest that understanding the positionality and politics of complete streets is essential in reaching their potential within a just mobilities future.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".