Mapping Massacre and Restorative Justice: A Study of Michael Ondaatje’s Anil’s Ghost
Bibliographic record
Abstract
The research article delves into human emotions, particularly during Sri Lanka's civil war, and it is analysed through the lens of Anil’s Ghost by Michael Ondaatje. It investigates the country's tumultuous condition caused by various separatist organisations, as depicted in Anil's Ghost. The focus of the research is to analyse the series of historical events that occurred in Sri Lanka in the aftermath of colonisation. The study explores the historical intricacies of the Civil War as reflected in the novel's primary narrative. The main emphasis is on the author's factual presentation, which increasingly emphasises the values of peace and solidarity of humanity. This examination analyses Gregory H. Stanton's final stage of genocide denial. It investigates the government's practice of concealing the corpses of deceased people's remains during armed conflict. The concept of mapping is brought with regard to the Massacre during war. The overarching goal of this research is to uncover the shortcomings of the Human Rights Commission within a nation embroiled in conflict. This endeavour brings attention to the inherent dangers faced by members of human rights organisations during the tumultuous period of Sri Lanka's civil war.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".