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Record W3155692188

Settlement Location Shapes Refugee Integration: Evidence from Post-War Germany

2019· article· en· W3155692188 on OpenAlexfundno aff
Sebastian Braun, Nadja Dwenger

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
FundersQueen's UniversityDeutsche ForschungsgemeinschaftQueen's University BelfastUniversity of ReadingUniversität MannheimUniversity of OxfordUniversity of St AndrewsUniversität TrierUniversität Hohenheim
KeywordsSettlement (finance)GermanRefugeeAgrarian societyWorld War IIDistribution (mathematics)GeographyEconomic integrationDemographic economicsEconomyPolitical scienceEconomicsAgricultureArchaeologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Following one of the largest displacements in human history, almost eight million forced migrants arrived in West Germany after WWII. We study empirically how the settlement location of migrants affected their economic, social and political integration in West Germany. We first document large differences in integration outcomes across West German counties. We then show that high inflows of migrants and a large agrarian base hampered integration. Religious differences between migrants and natives had no effect on economic integration. Yet, they decreased intermarriage rates and strengthened anti-migrant parties. Based on our estimates, we simulate the regional distribution of migrants that maximizes their labor force participation. Inner-German migration in the 1950s brought the actual distribution closer to its optimum.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.360
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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