High speed rail service and socio economic transformations in local areas, a review
Bibliographic record
Abstract
Many High-Speed Rail (HSR) projects exist in Europe and elsewhere in the world and generate many expectations of stakeholders in local economic areas. But their effects are controversial. There is a gap between academic literature (AL) and non-academic literature (NAL). The aim of the paper is to present a review of both kinds of literature because the effects of HSR are far from being a closed debate in AL. We highlight the relative proximity between AL and the stakeholders' expectations in local economic areas. We emphasize however the more general characteristics of the effects mentioned by NAL which appear to be a first explanation of the continuity of this controversy. We also show that AL identifies some extremely diverse and often more precise conditions for these effects, which are only partially presented in NAL. The area stakeholders omit the specificities of local areas and the largely contextualized effects and conditions. Another explanation could be that although AL puts the effects into perspective, it does not really question the effects. It supposes that, in specific cases, there will be effects if the conditions are present. The myth according to which automatic effects of High-Speed Rail on local economic development will occur can then continue to exist. But there is a risk to generalize conclusions that are linked to particular cases in terms of the economic situation, the geographical location, the quality of the service, attendance, specific resources and the actors' strategies, etc.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".