Commuting Between Border Regions in The Netherlands, Germany and Belgium: An Explanatory Model
Bibliographic record
Abstract
Border regions are often not very well connected to the national urban and economic centres and hence perform less well in terms of GDP per capita and unemployment. Cross-border commuting might be a way to improve the economic performance of border regions. This study explores the impact of a set of socio-economic, infrastructural or cultural explanatory variables that drive cross-border commuting in the Dutch-German-Belgium border regions for all outgoing commuters but also by gender, education and age. We found that cross-border commuting is a small-scale phenomenon, but the flows largely respond in the theoretically expected way to regional economic differences. Higher wages in the living region go together with lower cross-border outcommuting. More unemployment in the living region will make international outcommuting rise. Bordering regions with higher scores on the EU regional competitiveness index give lower international outcommuting. Quality of infrastructure does not show significant results. If the language on both sides of the border is the same, this gives more cross-border outcommuting. Males, medium educated and elderly workers show very similar outcomes as the model for all commuters, while cross border commuting of females and higher educates is hardly influenced by differences in the regional economic situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".