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Record W4410901017 · doi:10.3126/pursuits.v9i1.79359

A Refugee Study: Humanitarian Crisis in Sharon Bala’s The Boat People

2025· article· en· W4410901017 on OpenAlexaboutno aff
Kamal Rai

Bibliographic record

VenuePursuits A Journal of English Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeePolitical scienceRefugee crisisLaw

Abstract

fetched live from OpenAlex

This paper explores the plight of Tamil Refugees who are trapped to depart their ancestral land and suffer a humanitarian crisis through an analysis of Sharon Bala’s The Boat People. Tamil refugees have been living horrendously displaced lives from the early 1980s to the present in different parts of the world, particularly in Canada. This study uncovers the Tamil refugee humanitarian catastrophe to sustain their lives, resulting in a stateless population after being excluded from their inherited Land in Sri Lanka as a consequence of ethnic conflict. So, this paper adopts the theoretical frameworks of Giorgio Agamben, Hannah Arendt, and Thomas Nail to reflect the precarious situation of Tamil Refugees. Additionally, it embraces John Locke, Richard and Zapata Barrero to address this issue and the plight of Tamil Refugees. This paper concludes that Tamil immigrants are extremely dehumanized by portraying the obvious evidence of the protagonist Mahindan, who is intertwined in the perplexing legal process of Canada. Similarly, Tamil refugees are under a severe humanitarian crisis, thrown into such a situation to fight against the necessities of life, such as a lack of food, unsafe drinking water, insecure shelter, and harmful insects. While travelling, they have to take the risk of a life-threatening perilous journey on a wrecked boat and again inhuman imprisonment inside detention centres, to mistreatment after landing in Canada. Thus, they are in anxiety, frustration, pain, and misery, being excluded from the political sphere in both the Native and host countries.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.253
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
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

Citations0
Published2025
Admission routes1
Has abstractyes

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