Impacts on the Social Cohesion of Mainland Spain’s Future Motorway and High-Speed Rail Networks
Bibliographic record
Abstract
A great expansion of the road and rail network is contemplated in the Infrastructure, Transport and Housing Plan (PITVI in Spanish), in order to achieve greater social cohesion in 2024 in Spain. For this reason, the aim of this study is to classify and to identify those municipalities that are going to improve or worsen their social cohesion. To achieve this goal, the municipalities were classified according to the degree of socioeconomic development, and their accessibility levels were determined before and after the construction of these infrastructures. Firstly, the socioeconomic classification demonstrates that there is predominance in the northern half of the peninsula in the most developed municipalities. Secondly, the accessibility levels show that the same center-peripheral models are going to be kept in the future. Finally, poorly-defined territorial patterns are obtained with respect to the positive or negative effects of new infrastructures on social cohesion. Therefore, it is possible to state that the construction plan is going to partially fulfill its aim, since a quarter of the population is going to be affected by a negative impact on socioeconomic development. As a consequence, people who live here are going to have major problems in achieving social cohesion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".