MétaCan
Menu
Back to cohort
Record W3145102013 · doi:10.3390/socsci10040123

The Border Harms of Human Displacement: Harsh Landscapes and Human Rights Violations

2021· article· en· W3145102013 on OpenAlexafffund
Suzan Ilcan

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHuman rightsScholarshipNegotiationAgency (philosophy)Argument (complex analysis)Displaced personSociologyPolitical scienceLawCriminologyLaw and economicsPolitical economyRefugeeSocial science

Abstract

fetched live from OpenAlex

Building on the work of critical migration and border studies, particularly the scholarship on the suffering of displaced people through border-related violence, the article focuses on bordering practices and human rights violations relating to the Syrian civil war. It advances the argument that during peoples’ fragmented journeys to seek safety and protection within and outside of Syria, which are often punctuated by stops and starts, they encounter one or more of three kinds of bordering practices—hardening of borders, expansion of borders, and pushbacks—that can injure them and violate international human rights and often the principle of non-refoulement. The article refers to these encounters as the “border harms of human displacement”. The analysis emphasizes the experiences of people on the move and the cruelties and spatial violence they endure. The latter include lengthy periods of walking and running, travel across hazardous lands and seas, family separation, state restrictions, and mistreatment by border authorities. Yet, in response to such difficulties, they continue to assert their agency by negotiating bordering practices and harsh landscapes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.992

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 studies0.0090.001
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 head0.024
GPT teacher head0.382
Teacher spread0.359 · 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 designTheoretical or conceptual
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

Citations14
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSocial SciencesSame topicMigration, Refugees, and IntegrationFrench-language works237,207