Bibliographic record
Abstract
One of the many ironies of Indonesia’s self-perception as a rising power and an emerging market is that the world does not seem to reciprocate these feelings. It sometimes looks as if the archipelago is only on the map of Western oil and mining executives as well as board room members of ailing Canadian cell phone companies that are desperately trying to make up for lost market shares in rich countries. Developments in the world’s largest Muslim-majority country and third largest democracy, in other words, have largely gone unnoticed outside a small circle of area specialists and donor agency folks. This is also true for Indonesia’s decentralization program, one of the world’s most ambitious and radical initiatives. After the New Order dictatorship of President Suharto collapsed in 1998, Indonesia embarked on an ambitious decentralization program that initiated a restructuring of the country’s political institutions on a scale unprecedented since the 1960s. Two laws were adopted that spelled out the conditions for the devolution of political authority and outlined a new system of fiscal arrangements between Indonesia’s institutional layers. The implementation of these laws shifted a considerable amount of political authority to
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.892 | 0.880 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".