Bibliographic record
Abstract
Purpose – Shedding light on urban transportation and, more specifically, the contemporary development of “smart” bikesharing systems (i.e. short-term bicycle rental services), the purpose of this paper is to focus on Montreal's bikesharing experiment. Known as BIXI (a contraction of the words BIcycle and taXI) since its inception in 2009, this system has been exported to other cities around the world, making it especially relevant for the analysis of this innovative and sustainable form of urban mobility. Design/methodology/approach – By tracing the policy history of BIXI and the current political debate about its future while using a framework focusing on the role of ideas in public policy, the paper directly contributes to the literature on the growing role of bicycles in sustainable urban transportation. The qualitative analysis is based on a systematic review of government documents and BIXI-related articles published in the Montreal French- and English-language press. To complement this analysis and provide information about behind-the-lesson drawing processes leading to the creation of BIXI, six semi-structured interviews were conducted with officials in charge of bikesharing policy in Montreal, as well as in Boston and London, England, two cities that have adopted (and adapted) the BIXI model. Findings – This analysis stresses the role of lesson drawing and framing processes in the development of Montreal's bikesharing system. While it is clear that the technological and policy developments of BIXI illustrate systematic and positive lesson drawing, on the framing and public relations side, the Montreal experiment suggests it is politically risky to boost public expectations about the potential costs of bikesharing systems for taxpayers. In addition to their innovative and sustainable contributions to urban transportation and pro-bike strategies, bikesharing systems are public investments that are not necessary free of costs for taxpayers. Framing these systems as public investments rather than a “free ride” for taxpayers would be a more accurate, and potentially effective, way to promote their development in the context of the current push for sustainable transportation policy in cities around the world. Originality/value – What this paper offers is a sociological perspective on an emerging and important policy issue, through an original combination of lesson drawing and framing perspectives on policy development. Montreal's BIXI is one of the most discussed (and exported) bikesharing systems around the world, and this is the first detailed policy analysis devoted to its genesis and politics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".