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Record W4361011210 · doi:10.26522/ssj.v17i1.4157

“A Mass Exodus in Rebellion” – The Migrant Caravans: A View from the Eyes of Honduran Journalist Inmer Gerardo Chévez

2023· article· en· W4361011210 on OpenAlexvenueno aff
Soledad Álvarez Velasco, Nicholas De Genova

Bibliographic record

VenueStudies in Social Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansJournalismMass migrationFace (sociological concept)Resistance (ecology)Political scienceInterpretation (philosophy)Migrant workersEconomic historyHistorySociologyGender studiesLawEconomic growthSocial science

Abstract

fetched live from OpenAlex

This article analyzes the migrant caravans as a strategy of resistance to the war against migrants in transit to the United States, exacerbated during the pandemic. This is the edited transcript of an interview conducted with Honduran journalist Inmer Gerardo Chevez, correspondent of Radio Progreso. Having travelled the Central American and Mexican routes accompanying on foot the transit of thousands of migrants since 2018, Chevez is a notable eyewitness and expert in situ of the Caravans. The interview confirms that the caravan has become one of the premier forms in which Latin American migrants, including agricultural workers, struggle and their spatial dispute with the heterogeneous border control regime of the Americas are materialized. The text also reflects on the role that photography and critical journalism can play in the face of the contemporary anti-migrant policy turn. We conclude with an interpretation of the effects that the militarized violence against Latin American migrants in transit to the United States is having across the region.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.019
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.099
GPT teacher head0.438
Teacher spread0.339 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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