Who Knows? Oral History Methods in the Study of the Massacres of 1965-66 in Indonesia
Bibliographic record
Abstract
The massacres in Indonesia in 1965-66 must have numbered in the thousands but only a few have been studied in any detail. The scant literature about the massacres tends to focus on the historical context rather than on the massacres themselves. One reason for this lack of knowledge is that the massacres were designed to be mysterious. They were not public events; they were meant to be forgotten. To research them, one has to bore through decades of sedimented lies, legends, and silences. This essay reviews the contributions of oral historians to our understanding of the massacres. I argue many of the existing oral histories are flawed because their research methods are unsuited to a situation where the basic facts about the event being investigated are so poorly understood. Researchers have hardly known what to look for. Oral historians have focused their studies on local communities but have tended not to obtain information from a broad crosssection of “locals.” Obtaining reliable information about any single massacre requires an unusual level of cross-checking of information with a variety of people. The preference for strictly local studies has left the main perpetrator, the army high command in Jakarta, out of the picture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".