Cross-border mobility: Rail or road? Space-time-lines as an evidence base for policy debates
Bibliographic record
Abstract
Mobility and transport patterns in border regions are highly relevant topics, as these regions still tend to be areas of limited accessibility, embedded in complex political settings of transport policy. Moreover, the contemporary call for a transition to sustainable mobility applies also to border regions. Nevertheless, limited data availability and harmonization across borders hamper the debate. In this paper, we develop a methodological approach that builds on open-source data and allows for comparative analysis and visualization of cross-border mobility and accessibility. The key elements are “space-time-lines’, combined with an indexation approach. Our study aims to position the different means of transportation in border regions. More concretely speaking, we seek to answer three main questions: In which regional settings are rail or road connections quicker? Can we identify categories of accessibility patterns? How do domestic und cross-border accessibility relate? We respond to these questions with a rail and road accessibility analysis of German border regions from a comparative perspective. Our results show that (a) the catch-up process for cross-border accessibility is not yet complete and that (b) some regions show tunnel effects, as cross-border infrastructure improvements can bypass the border region in the local sense.
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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 | 0.041 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".