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Record W2958759727 · doi:10.1093/migration/mnz029

Emotional geographies of irregular transmigrants’ journeys

2019· article· en· W2958759727 on OpenAlex

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

VenueMigration Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversité de MontréalInternational Centre for Comparative Criminology
Fundersnot available
KeywordsShameTestimonialSociologySorrowKindnessCourageSocial psychologyPsychologyGender studiesPolitical science

Abstract

fetched live from OpenAlex

Abstract Migrants’ journeys involve geopolitical, corporeal, and emotional dimensions. Yet, emotions, which are fundamental to understand the migrant experience, are usually overlooked. Following the ‘emotional geographies’ approach, this article analyses the spatial contextualisation of the affective and emotional experiences of irregular migrants in transit. Cognitive mapping methodology is proposed as a means to address the spatial and subjective dimensions of migrants’ experiences. The ‘testimonial maps’ of two Central American transmigrants in Mexico are explored. The emotional geographies of irregular transmigration underscore the emotional turmoil associated with the irregular migratory process(es). They shed light to the familiar arrangements made before the journey, the natural landscape as part of the control, the encounters with agents of the state and criminal actors, the sanctuary places, the acquaintances and fortuitous friendships, the resilience and adaptability needed for endure the journey, and, beneath all this, the multi-emotional dimension of the journey: love, sorrow, shame, courage, anxiety, fear, trust, kindness, and hope.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.503
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.323
Teacher spread0.294 · 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