Monitoring the pulse of renewed Spanish waterfront cities through instasights
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
This study provides an analytical approach to using collaborative heatmaps from Instasights to gain an insight on the impact of renewed waterfront urban areas in terms of their relevant role in the perceived functional dynamism and livability of the city. The proposed method enables the identification of perceived functional thematic areas-districts, as Kevin lynch would refer to them in his work 'The Image of the city'-based on user-generated social media data, although the method adopted in this study diverges from that of lynch, which is based on fieldwork. Instasights is used as a research tool because the demo app collects, analyzes and visualizes data from a vast amount of social media. five waterfront Spanish cities-Madrid, Barcelona, Valencia, Bilbao and Zaragoza-have been selected as case studies to validate the method used. The main novelty of the study is the possibility of monitoring urban environments, even though they may be perceived as several thematic areas. The findings suggest that the proposed method is a valuable tool for gauging the pulse of waterfront areas through Instasight heatmaps. Moreover, differences in the way renewed public spaces are used and perceived, as well as overlapping functional areas are identified in the case study cities. The approach taken in this study provides a deeper understanding of the perception and complex dynamism related to the monitoring of the waterfront post-renewal phase, which enhances the study of urban renewal.
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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