Knowledge-based Economy (KBE) Frameworks and Empirical Investigation of KBE Input-output Indicators for ASEAN
Bibliographic record
Abstract
The purpose of this paper is to build a policy focused knowledge-based economy (KBE) framework based on the OECD KBE definition in order to identify the KBE factors in the Association of South East Asian Nations (ASEAN) region. The paper utilises the Beta coefficient technique which allows us to rank the most important KBE input factors to KBE output factors. After identifying KBE input-output factors, following the Australian Bureau of Statistics KBE framework assumptions, standardized beta coefficients are used to assess how many standard deviations a dependent variable will change, per standard deviation increase in the predictor variable. Standardization of the coefficient is usually done to answer the question of which of the independent variables have greater effects on the dependent variable in a multiple regression analysis, when the variables are measured in different units. Data are mostly collected from secondary sources such as the World Bank’s World Development Indicators and the International Institute for Management Development’s World Competitive Yearbook. The results show Singapore is the best performer in knowledge acquisition, production and distribution and the Philippines is the best performer in knowledge utilization. Indonesia, on the other hand, shows weak performance in almost all the KBE dimensions. The lessons from the success of Singapore and the Philippines for weak performance countries in KBE are to improve the efficiency of FDI inflows, to optimise the use of research and development expenditure, to increase the secondary school enrolment ratio and finally to increase the interaction between academia and industry, which facilitates the creation and commercial use of knowledge. This paper provides empirical evidence to rank the important KBE input factors that gives governments some insight on where to focus investment in order to become a successful KBE.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".