Migrating Vulnerability Marks Homo Sacer Harvest in Bala’s The Boat People
Bibliographic record
Abstract
Based on Sri-Lankan civil war and its numerous deaths, Sharon Bala’s novel, Boat People offers this study to examine the vulnerability of the immigrants and anti-refugee politics in Canada. Bala’s narrative of state induced misery that leaves no options to Tamil populations in Sri Lanka to seek refuge abroad offering space to investigate the state racism and human right hypocrisy in the global North. To assay the forcible dislocation of the refugees as a problematic, the study juxtaposes the war-ravaged Sri Lanka and the deceptive West’s deportation, letting this study peruse the state of homelessness of the politically ripped off Tamils homo sacer: the individual stripped of political and legal protections surviving a mere biological life; zoé in a state of exclusion from both law and society. Thus, the article interrogates the statelessness of the Tamils and their narrow escape, aligning with Giorgio Agamben’s homo sacer concept that spotlights the socio-politically abject life akin to Sri Lankan Tamils. While figuring out the existential struggle for dignified human position; biós. Protagonist, Mahindan and others make a herculean attempt for a refuge abroad, making a clarion cal for the humanitarian responsibility. Critiquing the westerner’s double-standard of human rights, the study shows their reduction to persona-non-grata, which scores high in humanities studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".