Racialized Hostipitality and Narrative Resistance in Sharon Bala’s <i>The Boat People</i>
Bibliographic record
Abstract
Sharon Bala’s The Boat People (2018) is at once a trauma narrative bearing witness to the ravages of Sri Lanka’s civil war, as well as a powerful portrayal of the racialized interpellation of refugee-immigrants from the Global South. A fictionalized account of the Sri Lankan refugees who arrived in Canada on board the ships Ocean Lady in 2009 and MV Sun Sea in 2010, the novel challenges the facile and depersonalized portrayals of asylum seekers that are proliferated uncritically in media. Bala presents a humane and humanizing counter narrative that challenges the arbitrary naming, shaming, and dehumanizing discourse that labels asylum seekers as “terrorists,” “illegals,” “thugs,” and “foreign criminals,” all labels used by Canadian anti-immigrant factions to brand the Sri Lanka asylum seekers in The Boat People. In this paper, drawing on the Derridian neologism “hostipitality,” I introduce the concept of “racialized hostipitality” to understand how Bala sheds light on the racialized and bifurcated nature of Canadian immigration law. By demonstrating how immigration law embeds both hospitality and hostility, and how hospitality is conditionally offered only to the “good immigrants,” I argue that Bala’s novel unveils the racialized hostipitality that conditions Canadian immigration law as portrayed in the novel.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".