Decomposition of Malaysian Production Structure Input-Output Approach
Bibliographic record
Abstract
Structural decomposition techniques are widely used to break down the growth in some variable into the changes in its determinants. Over the past two decades, input-output structural decomposition analysis (SDA) has developed into a major analytical tool. We review the development of SDA and its relationship to other methodologies. We present the fundamental principle of alternative approaches to deriving SDA estimating similarities and explore the various & decompositions of changes in I-0 tables. Using I-O Tables for the Malaysian Economy 1983-2000, this comparative study focuses on changes in the economic structure with different levels of development over time (1983-2000). The change in the economic structure is decomposed into two initial components (Technology and total output). According to the results, there are similarities over time in the national structure of production patterns of intermediate use of commodities. Also, the results indicate a rather remarkable degree of commonality in the patterns of growth processes, with more significant differences between sectors than between tables. However, the most changes within sectors, and the Malaysian table as a whole, seem to result from changes in x, and f. A seems to have remained relatively unchanged.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".