Improving urban accessibility: A methodology for urban dynamics analysis in smart, sustainable and inclusive cities
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
Despite the improvisations of current urban accessibility regulations and their application in urban systems, it is a fact that our cities are not accessible. Both, the assessment of the effectiveness of urban accessibility and its maintenance over time are issues that require a more consistent approach. In order to address these aspects, it is necessary to have an accurate awareness of the existing condition of urban accessibility. Therefore, the way this information is transformed into specific data, which must be collected, stored and assessed, is one of the main challenges that smart cities face. This research helps implement an integrated system for urban accessibility analysis, combining the latest advances in the Information and Communication Technologies, such as RF & GPS positioning, smart sensing and cloud computing. The main goal of this research is to develop a reliable and effective method to assess public space accessibility with special focus on people with disabilities, by eliciting from users personal experiences. Consequently, the data obtained will enable a better design for improving pedestrian mobility. As a result, a computational architecture for urban dynamics analysis has been designed. Finally, technology and data processing have been validated as an effective system for data collection, and, as a first approach to users' real experience, it has been proposed to have a testing scenario at the University of Alicante,.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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