Public Participation in the Process of Improving Quality of the Urban Frame
Bibliographic record
Abstract
The different tools for analysing the quality of public space are coincident when showing that a healthy urban space is conditioned by a proper urban structure. It is needed a balance between urban uses and a proper connectivity among the different parts of the city. At the same time, the users of the public space must have an adequate perception of the urban space it occupies; a good sense of identification of the user over the urban frame allows a proper use of the public space. The development of urban analysis in the city of Orihuela, using tools as RGBG Strategic Model, showed junk urban spaces around the Mount St. Michael. The application of the RGBG allowed the identification of social conflictive areas that had a limited connectivity with the rest of the city and inhabitants with a low feeling of identification with the closest urban area. The municipality of Orihuela used the knowledge acquired with these analysis tools for developing interventions over the public urban space focused on the detected social conflictive areas. With them, the municipality tried to improve the perception that inhabitants had over their closest urban environment. Public employment initiatives were developed with the participation of local inhabitants in order to integrate them in the process of recovering the conflictive urban areas. This paper describes the process developed and shows that once these interventions were finished, it is possible to validate the data obtained with the urban simulation tools. It is confirmed that the perception of the quality of public space is improved when the inhabitants are involved in the process of its recovery and when urban factors as connectivity are improved.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.003 |
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".