Local dimensions of global investment: Israeli property firms in Central Europe
Bibliographic record
Abstract
Transnational property investment has increased dramatically during the last few decades. This process has been traced by literature focusing on capital‐rich countries (e.g. the United States, Canada, Japan) and on major world cities. More recently, in tandem with the collapse of the Berlin Wall, the geographical horizons of foreign investors have broadened to include former socialist countries. This article examines the recent surge in Israeli property investment in Central Europe and argues that global flows depend on relationships between place of origin and destination. Mobility of property capital creates networks that connect cities on a transnational basis. Les investissements immobiliers transnationaux ont énormément augmenté au cours des dernières décennies. Cet aspect a été suivi et documenté surtout pour les pays riches en capitaux (Etats‐Unis, Canada, Japon, par exemple) et les grandes villes mondiales. Plus récemment, parallèlement à la chute du Mur de Berlin, les horizons géographiques des investisseurs étrangers se sont élargis aux anciens pays socialistes. L'article examine l'afflux récent d'investissements immobiliers israéliens en Europe centrale, affirmant que les flux planétaires dépendent des relations entre les lieux d'origine et de destination. La mobilité des capitaux immobiliers crée des réseaux qui relient des villes au plan transnational.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".