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Record W2612536551

Do Migrants Transfer Political and Cultural Norms to Their Origin Country? Some Evidence From Some Arab Countries

2017· preprint· en· W2612536551 on OpenAlexaboutno aff
Jamal Bouoiyour, Amal Miftah

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsReceiptRemittancePoliticsDemocracyPolitical scienceInequalityDevelopment economicsPerceptionDeveloping countryPolitical economyEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper explores some political and social consequences of international migration experience and remittance receipt in the case of Arab countries using Arab Barometer survey dataset. The main idea is to address whether persons who receive international remittances or have lived in the past in democratic host countries, namely U.S (or Canada) and Europe, can act as agents of changes. Three forms of political participation are considered comprising interest in politics, electoral participation and protest demonstration. Other indicators are taken into account including the perception of economic inequality and cultural constructions of gender in Muslim societies. We find that migration and remittance receipt have a positive influence on the political participation and interest of migrants and families who remain in the country of origin and receive remittances. Moreover, our estimates show that migration experience of male migrants strengthens their likelihood to vote, to be more interested in politics, to perceive the economic inequality as well as to encourage the veiling in their home countries. However, they seem less engaged in a protest demonstration.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.382
Teacher spread0.318 · 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
Published2017
Admission routes1
Has abstractyes

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