Rethinking and Moving Beyond GDP: A New Measure of Sarawak Economy Panorama
Bibliographic record
Abstract
Despite the relatively strong adjustment in the global economy outlook, the Malaysian economy remains uncertain as the ringgit movement lies ambiguously ahead while volatile capital flows, inflationary pressure, and the vulnerable external sector and global trade remain intense. The Sarawak economy, which relies heavily on primary commodities and export earnings from oil-based industries, will soon face a noxious mixture of economic risks following the decrease in commodity prices. Thus, it is essential to develop a well-timed signaling mechanism to estimate the unpredictable economic forces that develop from the complex and multidimensional issues of domestic and global economies. The ideology of indicator construction from the Conference Board will be applied in this study to build a composite leading indicator, called the Sarawak Business Cycle Indicator (SBCI), to trace the cyclical movement of the aggregate economic activity in Sarawak. In this respect, the SBCI, which has demonstrated statistical significance with an average leading power of 3.5 months, is expected to be important in reflecting a notable economic outlook for the State. More importantly, the SBCI will serve as a valuable reference to act as a short-term forecasting tool to provide insight at both the national and state levels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".