Place‐framing against automobility in Montreal
Bibliographic record
Abstract
In this paper, we examine the dynamic processes of place‐framing through struggles against automobility in Montreal, bringing place discourses in dialogue with mobilities scholarship. We explicitly consider the dynamics of place‐frames' development and emergence from the myriad of different meanings of activists engaged in a mobilisation, in interaction with the hegemonic transportation discourse. We analyse the mobilisation for car traffic‐calming in Montreal through three dynamic moments of place‐framing: naming and blaming (voicing a certain experience of place), claiming solutions in space and practising the desired place. Each moment consists of a discursive struggle through which selective meanings are being given to place in relation to mobility. In Montreal, these moments were crucially associated with new network connections: (1) a coalition around the association of car traffic with vulnerability, (2) the targeting of local regulators for traffic‐calmed places when dismissed by auto‐oriented space regulators and (3) performing a community of practice for walking and cycling in neighbourhoods, with tensions between the utopian local way of life and regional flows. This study demonstrates the relational linkages of mobility and place. Place‐frames are not immutably linked to existing landscapes and experiences, but are shaped through nodal and network articulations that emphasise communities of practice in (imagined and desired) place(s) of everyday movement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".