Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The widespread adoption of mobile phone and other location-tracking devices, and the enormous amounts of data they produce, has provided municipalities with the opportunity to automate previously time-consuming and labour-intensive data collection processes. Municipal planners, in particular, have begun to integrate the aggregated data sets of private urban technology platforms into active transportation and broader infrastructure planning initiatives. To date, however, there has been limited research on the implications of this integration for municipal decision-making and governance processes. Using the Strava Metro data stream and its free-access model as a case study, this paper explores both the motivations behind municipal adoption of the Strava platform and the benefits that accrue from its usage. Through the application of a mixed methods approach, including the building of a use case database via a search of internet and academic literature sources and qualitative interviews with municipal planning staff, our research examines how Strava data is used to support the work of municipal planners and evaluates the strengths and weaknesses of that use. Our study finds that Strava Metro data aided municipal staff in the planning of cycling and pedestrian infrastructure, complementing available in-house data sets; helped spur new active transportation initiatives; and enabled innovation and professional curiosity on the part of planners. The paper concludes by exploring the ramifications of Strava data for community wellness and broader public realm improvements, as well as extending a discussion with respect to the platform’s sociodemographic representativeness and related limitations.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it