The Trauma of the Civil Conflict on the Masses: An Analysis of Sharon Bala’s The Boat People
Bibliographic record
Abstract
The emergence of refugees and their problems remain a perennial and unresolved global issue till date. The consequences of the issues undoubtedly generate forced displacement, economic crises, and socio-cultural ramifications. The proposed study attempts to analyse the refugee crises in the pre and post civil war scenario with the help of select novel through the lens of trauma studies in literature. Sharon Bala’s The Boat People deals with the events of Sri Lankan ethnic war and the problems of asylum seekers in Canada. The study adopts the theoretical framework of truma in the arena of literary narratives. The characters of the novel are subjected to an investigation within the context of trauma studies, which enables a better understanding of the socio-political impact of the war. War atrocities generates fear of life, anxiety, forced displacements, uncertainity of life, statelessness, discrimination, and terror suspects, causing trauma. The select literary narrative in its characterization demonstrates the actuality of the Sri Lankan civil war and its after effect on the innocent civilians who became stateless and dispossessed in the war-torn nation. Further, it illustrates the myriad challenges encountered by the refugees in the host country. War is the core cause of refugee problems and their trauma. Ceasing war, rehabilitation, and restoration measures aid in controlling the refugee crises. In the last phase, the paper proposes a few adoptable measures to address the refugee problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".