Finalising the nation: The Indonesian military as the guarantor of national unity
Bibliographic record
Abstract
Abstract: The Indonesian military sees itself as the guarantor of national unity, the state's last line of defence against separatist movements. This paper argues that the military's methods for maintaining national unity have been counterproductive. Its counter‐insurgency wars in Aceh and Papua have exacerbated the sense of alienation from Indonesia that the people in these provinces have felt. In this post‐Suharto era of political reform, the military has been unable to recognise that its old methods have failed, even after its obvious failure in East Timor, whose people, after living under a 24‐year military occupation, rejected continued integration with Indonesia in a referendum in 1999. The fact that the politicians in the legislative and executive branches of the state have tended to encourage the military to persist with its old methods suggests that the military by itself should not be faulted. Only political resolutions, such as the Helsinki agreement for ending the conflict in Aceh – an agreement that resulted more from the devastation of the December 2004 tsunami than from the Indonesian military's counter‐insurgency warfare – offer any guarantee of national unity.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".