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Political Geographies of Migration: The Borders, from Static Lines to Mobile Entities

2020· article· en· W3048672410 on OpenAlexafffund
Mónica Poveda Romero

Bibliographic record

VenueTlalli Revista de Investigación en Geografía · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsWilfrid Laurier University
FundersUniversity of TorontoUniversity of Minnesota
KeywordsSovereigntyPoliticsRefugeePolitical scienceMobilitiesPolitical economyImmigrationPower (physics)Political geographySociologyEconomic geographyEconomyGeographyLawSocial scienceEconomics

Abstract

fetched live from OpenAlex

The article explores the border literature in political geography in order to understand the contemporary proliferation of bordering practices in the Western world. It takes the case of President Trump administration’s policies to show how borders can be concealed in social and political practices inside of sovereign territory. This expansion of geographical borders continually shapes the socio-spatial identities of migrants. The text also analyzes why the traditional bordering practice of building border walls is still an appealing resource aiming at keeping immigrants away from Western territories, even after the promise of a “borderless world” in the late 20th century. This article argues that the expansion of border walls is explained by the analysis of three factors: the transformations on the refugee protection framework after the 90s, the change in states’ perception of refugees as a threat to Western societies, and the fear of states to be perceived as actors non-capable to maintain their sovereignty. These contemporary practices are consistent with recent debates in border theory that see the border as a mobile entity instead of a static territorial line separating two units of land. This article aims at fostering the idea of border studies as a way to unveil new forms of power and control. It also pretends to foster an understanding of the interconnectedness of border practices around the world.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.337
Teacher spread0.314 · 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 designNot applicable
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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueTlalli Revista de Investigación en GeografíaSame topicCross-Border Cooperation and IntegrationFrench-language works237,207