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Record W2037198302 · doi:10.5539/ibr.v2n4p129

Decomposition of Malaysian Production Structure Input-Output Approach

2009· article· en· W2037198302 on OpenAlexvenueno aff
Hussain Ali Bekhet

Bibliographic record

VenueInternational Business Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsDecompositionProduction (economics)Structural changeTable (database)EconometricsFinal demandEconomicsComputer scienceMathematicsMacroeconomicsData mining

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.345
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2009
Admission routes1
Has abstractyes

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