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Record W4285676613 · doi:10.29173/crossings92

The Gendered Consequences

2022· article· en· W4285676613 on OpenAlexaff
Abigail Isaac

Bibliographic record

VenueCrossings An Undergraduate Arts Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRefugeeObligationImmigrationPolitical sciencePsychological interventionInternally displaced personIrregular migrationImmigration policyStakeholderCriminologyEconomic growthSociologyLawEthnology

Abstract

fetched live from OpenAlex

This policy paper explores the gendered vulnerabilities of migrants seeking refuge from unlivable conditions in El Salvador, Honduras, and Guatemala (Northern Triangle Countries), who are forced to the U.S.-Mexico border and stranded in border camps strung along U.S. entry ports. In particular, it examines the impacts of four immigration and border policies initiated by the Trump administration, and details the compounded risks of violence faced by displaced women and gender-diverse communities while traveling to the border and in border encampments. Migrants seeking asylum experience a range of physical and psychosocial trauma, and their safety is contingent on the varying stakeholder positions of the American and Mexican governments, transnational non-governmental organizations, and the United Nations High Commissioner for Refugees (UNHCR). In evaluating the barriers to accessing basic resources and legal support faced by marginalized communities at the border, I argue that conditions in-camp require gender-sensitive humanitarian interventions from the UNHCR as mandated by its ethical and legal obligation to protect the rights of asylum-seekers. Following my analysis of the gendered vulnerabilities in border encampments and their historical roots, I then propose three potential policy choice sets to address the increasing urgency of the crisis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0240.001
Scholarly communication0.0030.001
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 head0.043
GPT teacher head0.334
Teacher spread0.291 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCrossings An Undergraduate Arts JournalSame topicMigration, Refugees, and IntegrationFrench-language works237,207