KBE Frameworks and Their Applicability to a Resource-based Country: The Case of Brunei Darussalam
Bibliographic record
Abstract
Knowledge is generally considered to be one of the most important drivers of economic growth. The difference between a knowledge-based economy (KBE) and a resource-based one is that in the former, the main competition between individuals, firms, and countries is the ability to innovate. Other forms of competition, for example through pricing strategies and access to resources, become secondary. Generally, knowledge is information combined with technology that dramatically increases its impact when shared. Organizations such as the Organization for Economic Cooperation and Development (OECD), Asia Pacific Economic Cooperation Forum (APEC), Australian Bureau of Statistics (ABS) and the World Bank Institute (WBI) have developed different KBE frameworks to indicate the extent of countries’ knowledge base and implicitly to guide policy. But these frameworks have little in theoretical underpinnings and applying them universally across all countries in different regions, at different stages of development and with different institutional, social and economic characteristics may be misleading and result in inappropriate policy responses. In this paper we propose a framework that clearly distinguishes input-output indicators of a knowledge-based economy under four important dimensions: knowledge acquisition, knowledge production, knowledge distribution and knowledge utilization, and attempt to adapt them in a practical policy oriented approach for an economy like Brunei Darussalam, which is attempting to transform from a resource-based to a knowledge-based economy.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".