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Record W4387059409 · doi:10.1080/08865655.2023.2261455

Information Directed Towards Migrants and the (Un)Making of Borders: An Interdisciplinary Perspective Between Countries of Origin, Transit, and Destination

2023· article· en· W4387059409 on OpenAlexvenueno aff
Anissa Maâ, Julia Van Dessel, Amandine Van Neste-Gottignies

Bibliographic record

VenueJournal of Borderlands Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Context (archaeology)State (computer science)EnforcementControl (management)Public relationsPerceptionSociologyPower (physics)MobilitiesPolitical sciencePolitical economyLawEconomicsGeographySocial science

Abstract

fetched live from OpenAlex

Migration information campaigns and awareness-raising activities are increasingly used by Western governments as a "soft" tool of border enforcement in countries of origin, transit, and destination. Acting upon perceptions and aspirations, these information provision initiatives aim at convincing (potential) migrants to remain in or "voluntarily" return to their country of origin. As they rely on security and humanitarian rationales, they gather heterogenous actors whose practices oscillate between migration control and assistance. Yet, despite their apparently consensual nature, these initiatives bring out conflicting interests and generate contestations on the ground. In this perspective, this SI approaches information as a highly crowded and disputed field to grasp the complexity of power relationships in a restrictive migration context. Drawing on an interdisciplinary perspective, it investigates the discourses and norms conveyed by governmental initiatives that use information as a tool to control mobilities; the communication strategies defined by state and non-state actors to reach (potential) migrants; and the everyday practices deployed by migrants themselves to navigate this disputed information landscape.

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)0.001
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 score0.251
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.387
Teacher spread0.358 · 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 designQualitative
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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

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