Public space regeneration strategies: The case of salou
Bibliographic record
Abstract
Salou, which is one of the many highly specialized tourist resorts located on the Spanish Mediterranean coast, is a hundred kilometres south of Barcelona.Within its narrow boundaries of 1.481 ha, Salou hosts 7.4 million overnight stays per year and is home to 52 hotels.A ratio which ranks Salou amongst the tenth most visited municipalities in Spain [1].Distance from Salou's historical town centre, the area of Carles Buigas Avenue (CB) emerges as being the heart of the municipality's tourism and leisure industry.Salou developed, as did so many other Spanish coastal touristic locations, during the sixties and seventies as a consequence of the increasing demand for sun and beach destinations amongst the European and Spanish middle classes.Unfortunately, the "ageing" of this built up area clamours for close attention today.The visible physical degradation of the property is becoming a cause for concern and preoccupation amongst the main property owners and investors: public administration, hotel managers, shopkeepers and neighbours.Hotels emerge as the key problem within the set physical boundaries of this study.They occupy approximately 50% of the total land surface, 28 out of a total of 52 hotels within the town being concentrated in that area.This accumulation of hotels also breaks the particularity of the predominance of second residences which is so customary along the Spanish Coast.This paper delves into the data and proposals obtained from analysing the public space of the CB area.Similarly, as a consequence of the previous analysis, a set of proposals for intervention are also presented.The proposals are conceived to be developed within different time scales, in response to political and social willingness and economic capacity.The objective of the work is to induce an urban and tourism paradigm shift in the area, thus facilitating the emergence of a new tourism model.Solutions are urgently needed to provide specific answers to a particular scenario, which has similitudes to those of other Mediterranean Coastal Developments specialized in tourism activities, which too, after being operative for more than forty years, are suffering from deterioration or abandonment.Despite it still being an open process, the study understands that due to the complexity of the committed task and the scale of the area, the goal will require the active commitment and collaboration of the property owners (administration, hotel managers, investors and neighbours).
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".