Perspectives on Cross-Border Labor in Europe: “(Un)familiarity” or “Push-and-Pull”?
Bibliographic record
Abstract
A common explanation for the incidence and development of cross-border labor are cross-border economic disparities and uneven economic developments: in border regions with high levels of cross-border labor, important growth poles with high wages and employment opportunities at a short distance at one side of the border, attract workers from a less developed side. Recently, however, geographers Henk van Houtum and Martin van der Velde have argued that this can only be part of the story. Because of “unfamiliarity” with life in bordering nation states there are invisible mental “thresholds of indifference,” that prevent an orientation towards the other side and an optimal allocation of labor across borders. In this collection of articles my co-editor, Martin Klatt, and I want to assess how these two approaches can be balanced in research on cross-border labor markets in Europe. Is it possible to overcome the inherent tension between them? We will address the historical impact of state borders on cross-border labor mobility in borderlands. When could mental barriers of “unfamiliarity” be overcome by localized “push-and-pull”? In what circumstances could the full effect of “push-and-pull” be hampered by “unfamiliarity”?
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".