High speed railway and metropolitan integration in Spain: the case of Ciudad Real and Puertollano
Bibliographic record
Abstract
Ciudad Real and Puertollano, two small Spanish cities approximately one hours travelling time from Madrid on the Madrid-Sevilla High Speed Train (HST) line, are of particular interest in the study of the impact of HSTs on such centres of population. Both cities had been separated from the main transportation corridor between Madrid and Andalusia since the end of the 18th Century and, at the end of 1992, the HST line reintegrated them into this corridor. That same year the Autopista de Andalucia (Andalusia Freeway), whose route runs some 50 kilometres to the east of Ciudad Real and Puertollano, entered into service. The current article has three basic objectives. The first one is to determine the most appropriate type of methodology to analyse the effect produced by an HST ten years after its inception. The second is to describe the observed changes in the mobility patterns of the inhabitants of Ciudad Real and Puertollano. The article concentrates on the employment profile and the frequency of use of those who habitually travel on the HST to and from Madrid. Thirdly, the article analyses the role the HST has played firstly in integrating the two cities with each other, and secondly in integrating both with the metropolitan area of Madrid. It then analyses the distinct effects of each of the two transport routes (freeway and HST) on each of the two cities, paying attention to their economic peculiarities (Ciudad Real being a tertiary city and Puertollano being an industrial one).
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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".