Intelligence Capacity and Mass Violence: Evidence From Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What explains regional variations in the frequency and form of mass categorical violence? I first develop then test, via process tracing, a theory to answer this question. Employing process tracing in Central Java during the 1965–66 Indonesian Killings, I argue that these variations are conditioned by state intelligence capacity. Low intelligence capacity forces troops to rely upon civilian elites for information. This provides opportunities for civilian elites to widen targeting criteria, increasing the number of victims. Due to logistical constraints, security forces are also more likely to opt for lethal violence when they have low intelligence capacity, as they frequently struggle with caring for such large numbers of detainees. I further illustrate these findings by comparing the provinces of West Java and East Java. Data for this project is drawn from diplomatic archives, internal military publications, and a series of interviews with victims and participants in the Indonesian Killings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it