Bibliographic record
Abstract
Summary The president reconstituted his cabinet in early December, focusing primarily on the economic team. The highly experienced Boediono was appointed as Coordinating Minister for Economic Affairs, while Sri Mulyani Indrawati was promoted from chair of the planning agency to become Minister of Finance. The former economics coordinating minister, Aburizal Bakrie, was made Coordinating Minister for Social Affairs—out of the economic limelight, but still with considerable influence. These changes overall have been well received by the markets, as indicated by a considerable strengthening of the rupiah. The challenges facing the new cabinet remain immense, however. On a range of macroeconomic variables, performance has fallen well short of the government's targets. Output growth declined to just 4.9% p.a. in the December quarter from 6.5% a year earlier. Investment growth has fallen to a very low level, giving rise to concern about the creation of job opportunities. In a booming global economy, exports grew by only 7.4% in the four quarters to December, despite Indonesia's wealth of natural resources. The inflation rate doubled from September to October to almost 18% p.a., although subsequent price increases have been much slower. The president's anti-corruption campaign continues to generate much attention. The number of corruption cases involving government officials and state enterprise managers brought to the courts continues to increase. Some high-profile cases have resulted in convictions, but others have not. The campaign seems likely also to be extended to judicial reforms, which are clearly crucial, but one urgent issue yet to be tackled directly is the widespread suspicion that funding of major political parties derives largely from abuse of power by government officials. Numerous floods and landslides early in 2006 resulted in deaths, injuries and considerable physical damage. The frequency of such natural disasters has risen significantly over time, suggesting that governments at all levels need to develop mechanisms to manage them and implement policies to mitigate or prevent them. Reconstruction progress in Aceh and Nias during 2005 was disappointingly slow. The reconstruction authority predicts dramatic improvement in 2006, but there appears to be a need to clarify the relationships among its three components, and to make some adjustments to its master plan, particularly in relation to land use planning. A reallocation of available funds among major activities, better coordination of implementing organisations, and a rethinking of conflicts between the authority's roles as implementation agency and coordinating agency may all be needed if the ambitious and urgent targets are to be met.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".