BiciZen: Lessons in the Development of a Crowdsourcing Mobile App to Make Cities More Bikeable
Bibliographic record
Abstract
Improving bikeability is an urban policy goal that cities are pursuing to reduce their transport-related carbon emissions. To support this goal, this paper introduces BiciZen: a collaborative platform that aims to make cities and regions more bikeable. We describe the lessons learned from the development of this citizen science project. BiciZen is a mobile phone app that allows users to crowdsource information about their cycling experiences and suggest improvements to cycling infrastructure as well as report positive cycling experiences. BiciZen is open to concerned cyclists, city planners and researchers who wish to document and study cycling phenomena, including patterns of bicycle flows or participation in cycling events. The process of developing BiciZen highlights critical trade-offs pertaining to functionality, speed, cost, and flexibility. We found that when deciding what to include in the platform, the interests of researchers, users and city leaders did not necessarily align. We learned that feedback processes are valuable but highly resource intensive. Less than a year after the launch of BiciZen, we find that uptake has been highest in low-cycling contexts and driven mostly by a small number of super-users. The data collected on the BiciZen platform will provide a historical record of cycling incidents, events, and commentary that can be consulted by all stakeholders, and help advance co-creation and citizen science in the realm of active travel and bicycle mobility.
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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 | 0.059 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".