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Record W4402174905 · doi:10.1177/01979183241275469

The Migration Intersections Grid: An Organizing Framework for Migration Research in and through the Twenty-first Century

2024· article· en· W4402174905 on OpenAlex
Amina Maharjan, Ángel Del Valle, Annabel Erulkar, Arabinda Mishra, Catherine Steidl, Chandni Singh, Deepshikha Sharma, Fernando Riosmena, Gabriela Pinillos, Guy Abel, Jack DeWaard, Jasmine Trang Ha, Katharine M. Donato, Nyovani Madise, Raphael Nawrotzki, Rene Nevarez, Robert McLeman, Salma Abou Hussein

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.

Bibliographic record

VenueInternational Migration Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsWilfrid Laurier UniversityWestern University
Fundersnot available
KeywordsEconomic geographyGridRegional scienceGeography

Abstract

fetched live from OpenAlex

For this special issue of the International Migration Review, we develop and provide a comprehensive organizing framework, the Migration Intersections Grid (MIG), to inform and guide migration research in and through the remainder of the twenty-first century. We motivate our work by conducting a high-level scoping review of summaries and syntheses of different directions of travel in migration research over time. Informed by these results, we then identify and describe 12 components that constitute the MIG, which, as we later discuss, is an interactive intersectional organizing framework. Finally, we illustrate the MIG's interactive intersectional nature by applying it to several areas of migration research where a comprehensive organizing framework of this sort is needed to address existing and emerging issues and questions now and in the coming decades.

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)0.003
metaresearch head score (Gemma)0.001
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: Commentary · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.070
GPT teacher head0.430
Teacher spread0.360 · 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