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Record W4282932762 · doi:10.1332/rfxw5601

Human migration in a new era of mobility: intersectional and transnational approaches

2022· article· en· W4282932762 on OpenAlexaff
Alison Mountz, Shiva S. Mohan

Bibliographic record

VenueGlobal Social Challenges Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Northern British ColumbiaWilfrid Laurier University
Fundersnot available
KeywordsPerformative utterancePolitical scienceSociologyHuman rightsPolitical economyGender studiesHuman migrationInclusion (mineral)Development economicsPopulationLawEconomics

Abstract

fetched live from OpenAlex

This review article posits human migration as one of the most pressing social challenges of our time. We argue that challenges associated with migration and displacement will persist if their governance continues in piecemeal, performative and nationalist fashion, with the privileging of resource investment in national border fortification over addressing the root causes of migration and displacement. Advocating for intersectional and transnational approaches, we review some of the important, interdisciplinary dimensions of migration as a phenomenon that touches on every facet of human life. We then discuss how different groups of people on the move struggle with structural barriers to migration, as they attempt to access and then settle into new communities, and the challenges to inclusion and integration encountered in so-called host societies. Topics of discussion include borders and geographical divides, gender, sexuality, race, class, labour, displacement, rights, access and climate-induced migration.

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0040.020
Scholarly communication0.0110.017
Open science0.0020.012
Research integrity0.0030.004
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 head0.079
GPT teacher head0.322
Teacher spread0.242 · 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 designTheoretical or conceptual
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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Social Challenges JournalSame topicMigration, Refugees, and IntegrationFrench-language works237,207