Innovative Asia: Advancing the Knowledge-Based Economy - Highlights of the Forthcoming ADB Study Report
Bibliographic record
Abstract
[Excerpt] The development of knowledge-based economies (KBEs) is both an imperative and an opportunity for developing Asia. It is an imperative to sustain high rates of growth in the future and an opportunity whereby emerging economies can draw from beneficial trending developments that may allow them to move faster to advance in global value chains and in position in world markets. Over the last quarter of a century, driven mostly by cheap labor, developing countries in Asia have seen unprecedented growth rates and contributions to the global economy. Sustaining Asia’s growth trajectory, however, requires developing economies to seek different approaches to economic growth and progress, especially if they aspire to move from the middle-income to the high-income level. KBE is an important platform that can enable them to sustain growth and even accelerate it. It is time for Asia to consolidate and accelerate its pace of growth. Asia is positioned in a unique moment in history with many advantages that can serve as a boost: to name a couple, an expanding middle of the pyramid—Asia is likely to hold 50% of the global middle class and 40% of the global consumer market by 2020; and the growing importance of intra-regional trade within Asia, increasing from 54% in 2001 to 58% in 2011. Many developing economies are well placed to assimilate frontier technologies into their manufacturing environment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".