Bibliographic record
Abstract
A major realignment is taking place in the way we understand the state in Indonesia. New studies on local politics, ethnicity, the democratic transition, corruption, Islam, popular culture, and other areas hint at novel concepts of the state, though often without fully articulating them. This book aims to capture some dimensions of this shift. One reason for the new thinking is a fresh wind in state studies more generally. People are posing new kinds of questions about the state, and they are developing new methodologies to answer them. Another reason for this shift is that Indonesia itself has changed, probably more than most people recognize. It looks more democratic, but also more chaotic and corrupt, than it did during the militaristic New Order of 1966–98. This book consists of case studies from many different settings around the archipelago. The studies focus on various types of state representatives, such as village heads, informal slum leaders, district heads, and parliamentarians. They explore the spaces and settings where the state is evident and where it is discussed: coffee houses, hotel lounges, fishing waters, and streetside stalls. They investigate state authority, both as a set of actual practices and as an image of what the state “ought” to be. The case studies, and the broader trend in scholarship of which they are a part, allow for a new theorization of the state in Indonesia that more adequately addresses the complexity of political life in this vast archipelago nation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".