Bibliographic record
Abstract
Over the past four decades, Singapore have achieved remarkable economic growth by applying outward-oriented development strategies that include actively hosting foreign direct investment(FDI) through trade liberalization. The crucial contribution of the Singaporean government has been to provide an efficient infrastructure, tax incentives and a workforce attractive to foreign investors. The government has adopted an activist industrial policy, promoting sectors and firms thought to have high growth potential, as well as selectively intervening for flexible factor markets and macroeconomic stability. The purpose of this study is to analyze the distinctive features of Singapore`s industrial development strategy compared to the other Asian Newly Industrializing Economies and to examine major new challenges which Singapore faces in the current recession. The Singapore`s economy grew at an average annual rate of 8.5% over 1986-1998. Share of financial and business services sectors in total GDP grew from 20% in 1986 to 26% 1998. It`s economy grew by a strong 9.9% in 2000, a further improvement from 5.9% in 1999. The World Competitiveness Yearbook 2001 produced by the International Institute for Management Development(IMD) ranked Singapore the second most competitive economy in its Overall Competitiveness index. However, affected by the adverse external environment, Singapore`s economy fell into a technical recession in the second quarter of 2001. GDP fell by 5.4% in the third quarter and the manufacturing sector was down by a hefty 18.9%, the sharpest decline on record. Singapore has remained competitive overall, but highlights the importance of improving its productivity in technology and human capital which are becoming the main growth drivers in the knowledge based economy. Driven by the forces of globalization and rapid technological developments, the basis of wealth creation in the post=crisis era will shift from traditional factors of production to the ability to access, harness and apply knowledge. To position itself for this knowledge age and become an advanced and globally competitive knowledge-based economy within the next decade, the Singapore`s economic policies in this phase will centre on knowledge intensive activities charted by the Economic Review Committee(ERC). This papers concludes that Singapore`s future challenge is to advance into an economy where the driving forces of growth are knowledge or intellectual capital, a high-quality work force, and an innovative private sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.153 | 0.111 |
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".