Bibliographic record
Abstract
According to Jayasuriya (2004), Sri Lanka – known as Ceylon until 1972 – was characterized by impressive social welfare in the 1940s, 1950s, and 1960s but has experienced intense social warfare since the 1970s. In this chapter, I explore whether the two are causally related. I begin by reviewing the country's history of ethnic violence and educational expansion. Next, I analyze whether educated individuals contributed to ethnic violence in influential ways. Finally, I investigate whether any of the educational mechanisms promoted ethnic violence. Ethnicity and ethno-nationalist violence in Sri Lanka Sri Lanka is located on an island off the southern tip of India and is inhabited by 20 million people from diverse linguistic and religious backgrounds. The Sinhalese are the largest ethnic community and comprise nearly three-quarters of the island's total population. The Sinhalese speak Sinhala, an Indo-European language, and are nearly all Buddhist. Their progenitors emigrated from India approximately 3,000 years ago and gradually absorbed other peoples (including communities already living on the island and other more recent immigrants from the Indian mainland). Although sharing the same religion and language, the Sinhalese are divided between the Kandyan and Low-Country Sinhalese, an internal division with only limited social significance that resulted primarily from geography and historical differences in the extent of European colonial influence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".