Impacts of Smart Configuration in Pedelec-Sharing: Evidence from a Panel Survey in Madrid
Bibliographic record
Abstract
Despite the recently increasing research interest, this is one of the first studies employing a panel sample of users and nonusers to understand the bike-sharing phenomenon ( N=205 ). On the basis of a novel surveying technique, a case study on the clients of the state-of-the-art bike-sharing scheme of Madrid (Spain) is presented. BiciMAD is a system of the latest generation, namely, multimodal demand responsive bike-sharing: a fleet of electric pedal-assisted bicycles (pedelecs) with an advanced technology and unique smart service configuration to tackle challenges that may hinder the promotion of cycling and bike-sharing in the city. A statistical test has verified that there is a moderate association between previous intention and actual use of bike-sharing (Cramer’s V = 0.25) and both barriers and motivators of further use have been identified. Indicators on mobility patterns show that although drawing primarily from other sustainable modes of transport, bike-sharing has increased mobility (total number and distance of trips) and especially active travel but decreased the perceived travel time.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".