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Record W3159484572 · doi:10.1177/00219096211008468

Non-Conventional Migration: An Evolving Pattern in South Asia

2021· article· en· W3159484572 on OpenAlexaff
AKM Ahsan Ullah, Mallik Akram Hossain, Ahmed Shafiqul Huque

Bibliographic record

VenueJournal of Asian and African Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRemittanceHuman migrationPersecutionDevelopment economicsPoliticsIrregular migrationForced migrationPovertyGeographyPolitical scienceNatural disasterImmigrationRefugeeEconomic geographyPopulationSociologyEconomics

Abstract

fetched live from OpenAlex

The circumstances prevailing in South Asia (SA) have led to a plateauing migration stream that has resulted in several categories of migrants. The underlying factors driving migration have been identical in all the countries of SA. In recent years, however, poverty, conflicts, political and religious persecution, natural disasters and climate change have emerged as the most prominent drivers. External migration flow from SA has more than doubled between 2000 and 2015. This is a dynamic region, with millions (over 38m in 2017) of people crossing borders, both intra-regionally and extra-regionally. In recent years, wealthy citizens from SA have begun to move out of their countries with the intention of settling down elsewhere. This tendency has raised concerns among the policy makers because they create the grounds for reverse remittance flows. This research is meant to identify and contribute to the discourse of a new category of migrants (non-conventional migration) who are different from those in the conventional migration stream that included economic and forced migration. This research has crucial policy implications for both origin and destination countries.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.763

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 head0.025
GPT teacher head0.311
Teacher spread0.286 · 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 designQualitative
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

Citations17
Published2021
Admission routes1
Has abstractyes

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