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Record W3184614167 · doi:10.1080/08865655.2021.1948897

Migrations and Borders: Contributions to Understand Mobility in Cross-border Areas

2021· article· en· W3184614167 on OpenAlexvenueno aff
Marcela Tapia Ladino

Bibliographic record

VenueJournal of Borderlands Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsMobilitiesTransnationalismSociologyWork (physics)PopulationEconomic geographyPolitical scienceGeographyRegional scienceHumanitiesDemographySocial sciencePoliticsArtLaw

Abstract

fetched live from OpenAlex

This work is the result of two research projects designed to present a more precise definition of population movements in border areas. To that end, they include a review of topics such as migration, borders, transnationalism and the mobility paradigm. In this analysis, we verify the central role that the notion of migration has played in studies of international and border displacements and highlight the need to propose a more precise definition. Thus, based on the notion of social practices – of different types – coined by Abelardo Morales (2010. Desentrañando Fronteras Y Sus Movimientos Transnacionales Entre Pequeños Estados. Una Aproximación Desde La Frontera Nicaragua-Costa Rica. In Migraciones Y Frontera. Nuevos Contornos Para La Movilidad Internacional, ed. M.E. Anguiano, and A.M. López, 185–224. Barcelona: Icaria), we define these practices as cross-border insofar as they involve two or more national states, which give rise to a series of adjectival mobilities. These can be formal or informal and for different reasons, i.e. healthcare, leisure, trade or work, among others. Likewise, we verify that both cross-border social practices and adjectival mobilities are factors that generate cross-border mobilities in border areas.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.037
GPT teacher head0.450
Teacher spread0.413 · 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 designObservational
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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