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Record W4403552283 · doi:10.14712/12128112.3402

Transit Migration

2015· article· en· W4403552283 on OpenAlexaboutno aff
Evrim Hikmet Öğüt

Bibliographic record

VenueLidé města · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

As a result of the expanding human mobilization in the today’s world due to wars, workforce markets, etc., the scope of migration and diaspora studies has increased in many areas. This is also true for musical studies, where a growing body of literature has been produced about these issues. However, by focusing mainly on settled communities and their musical productions, such as hybrid genres and forms created in the destination land, this literature does not adequately cover “transit migration”. Transit migration, being a particular type of human mobility, refers to the migration that includes at least three or more steps. This means that transit migrants do not permanently inhabit the land they firstly enter as migrants, but are supposed to stay in this transit country for a while and then continue their journey in order to reach a final destination point. In this article, I deal with this specific type of migration based on my intense field study on the Chaldean-Iraqi migrant community in Istanbul. The Chaldean community in Iraq, as a religious minority, is one of the most affected groups in the ongoing situation in Iraq, especially after the US invasion in 2003. Turkey functions as a transit country on their way to their prospective destination points, mainly including the US, Canada, and Australia. While dealing with the role of music during the indefinite time period that the participants are in the process of being temporary inhabitants in a foreign land, the applicability of the theoretical concepts of permanent migration to temporary migration is also discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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