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Record W2996109509 · doi:10.1080/08865655.2019.1700387

Bound within Borders or Free as a Bird? Karen Life in Refugee Camps on the Thai–Burma Borderlands

2019· article· en· W2996109509 on OpenAlexvenueno aff
Jessica Bird

Bibliographic record

VenueJournal of Borderlands Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeGeopoliticsAgency (philosophy)PoliticsNarrativeGender studiesSociologyPerspective (graphical)Political scienceTransnationalismPolitical economyLawSocial science

Abstract

fetched live from OpenAlex

Some residents of Thai–Burma border refugee camps regard themselves as caged birds—as prisoners of war—with little or no freedoms. External imaginations of camp residents as bound within borders are also common. This paper provides another perspective. It argues that residents subvert camp limitations; it describes how they stretch life beyond borders. Despite their armed monitoring, camp borders are contested by outwardly connecting, using tactics of transnationalism and translocalism; in essence, cross-border mobility. Such tactics provide freedom in an otherwise incarcerated camp-life and empower people in an otherwise powerless world. They enable individual actors to change their socio-political narrative. Naturally, severely isolated and restricted camps have low morale and residents have little hope. Given the relative permanence of Thai–Burma border camps (now in their fourth decade of existence), and an annual downward trend in their international aid, I recommend policy that ensures consistently clear channels of connection to life beyond camps’ borders. This paper contributes to the borderlands debate by adding camp borders as another framework for investigation; it reflects on geopolitical contexts and shifting political landscapes impacting on cross-border mobility; and underscores the agency and self-determination of the Karen people from Burma.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.347
Teacher spread0.313 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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