Bibliographic record
Abstract
Every May 18, mourners gather near the sandy beaches of Mullivaikkal, a small strip between Chundikulam and Mulltaitivu in the Northern province of Sri Lanka, to commemorate the 2009 genocide against the Tamils. Mullivaikkal is where approximately three hundred thousand Tamil civilians found refuge as they fled the military bombardment between January and May 2009.1 Starting in 2010, the remembrance day commemoration attracts thousands of locals, coming together near the beach to reflect and remember. Increasingly, the commemoration also attracts transitional justice experts, along with diplomats and international governmental organization workers. In my contribution, I reflect on the work of the local and diaspora Tamil transitional justice experts as they begin to gather evidence from the families of victims for the newly created 2024 Commission for Truth, Unity and Reconciliation. Drawing on Homer's The Odyssey and the story of the “lotus eaters,” I frame these experts as “truth eaters,” preoccupied with collecting victim narratives for the purpose of personal gratification. As they engage in the repeated collection of particular elements of the victims’ truth—elements predicated on the demands of the field of transitional justice—the truth eaters are oblivious to the root causes of the war. I explain how attention to root causes through a Third World Approaches to International Law (TWAIL) lens can avoid the effects of the dominant liberal modes of truth seeking reflected in the work of these truth eaters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.029 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".