Bibliographic record
Abstract
In the late 1990s, Indonesians experienced unprecedented levels of ethnic conflict. During 1995 and 1996, riots in Situbondo, Tasikmalaya, and other parts of Java caused numerous deaths and the destruction of private property. They had ethnic and religious overtones that suggested serious tensions cutting across several dimensions of relations between Indonesia's ethnic groups. The wave of riots was perceived, in part, as a new phase in anti-Chinese sentiments that had regularly led to violence against the Chinese before and after Indonesia's independence. The targeting of places of worship, however, had others argue that relations between religious groups were deteriorating and reaching dangerous levels of tension. In either case, the riots were sufficiently different, numerous, and larger in scale from violent events since the 1960s to suggest a worrying trend. These worries were confirmed in the following years. Between 1997 and 2002, at least 10,000 people were killed in ethnic violence throughout the archipelago. In 1996–97 and 2001, two waves of violent clashes between Dayaks and Madurese in West and Central Kalimantan led to the deaths of at least 1,000 people and the displacement of hundreds of thousands of Madurese. In Maluku, at least 5,000 people were killed in a war between Christians and Muslims that began in January 1999 and escalated during the following three years. In East Timor, approximately 1,000 people were killed and 200,000 displaced in violence against the civilian population, following a referendum in August 1999.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.252 | 0.122 |
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".