Bibliographic record
Abstract
For decades almost the only social scientists who visited Indonesia’s provinces were anthropologists. Anybody interested in politics or economics spent most of their time in Jakarta, where the action was. Our view of the world’s fourth largest country threatened to become simplistic, lacking that essential graininess. Then, in 1998, Indonesia was plunged into a crisis that could not be understood with simplistic tools. After 32 years of enforced stability, the New Order was at an end. Things began to happen in the provinces that no one was prepared for. Democratization was one, decentralization another. Ethnic and religious identities emerged that had lain buried under the blanket of the New Order’s modernizing ideology. Unfamiliar, sometimes violent forms of political competition and of rentseeking came to light. Decentralization was often connected with the neo-liberal desire to reduce state powers and make room for free trade and democracy. To what extent were the goals of good governance and a stronger civil society achieved? How much of the process was ‘captured’ by regional elites to increase their own powers? Amidst the new identity politics, what has happened to citizenship? These are among the central questions addressed in this book. This volume is the result of a two-year research project at KITLV. It brings together an international group of 24 scholars – mainly from Indonesia and the Netherlands but also from the United States, Australia, Germany, Canada and Portugal.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".