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Record W3157815495 · doi:10.1177/0022343320980803

Emerging diasporas: Exploring mobilization outside the homeland

2021· article· en· W3157815495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Peace Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHomelandDiasporaMobilizationRefugeeArgument (complex analysis)EmigrationPolitical sciencePoliticsIdentity (music)PopulationPolitical economyEthnic groupHomeland securityCollective identityDevelopment economicsSociologyDemographyLawMedicineTerrorismEconomics

Abstract

fetched live from OpenAlex

Abstract An enormous number of people are leaving their homelands around the world today. This has happened several times in the past, but migration has spiked in recent years. These population movements can have significant effects on both the host country (where emigrants or refugees settle), as well as politics back in the homeland. After they leave their homelands, why do some groups mobilize, and in what ways? In this article, we examine a number of factors that may impact when emigrated groups mobilize after they move. We develop a new dataset on potential diasporas in the United States to evaluate a series of hypotheses, including those about motivations for mobilization such as identity maintenance, the objective plight of co-ethnics in the homeland, and group capacity to mobilize. We find some merit in the identity preservation argument and a strong effect of geographic concentration of the diaspora segment. Surprisingly, diaspora mobilization does not appear to be strongly related to conflict in the homeland among these groups.

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.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.286
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.299
GPT teacher head0.473
Teacher spread0.174 · 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