總要素生產力、知識經濟與工業先進國家之經濟發展--對臺灣經濟發展之啟示
Bibliographic record
Abstract
[[abstract]]根據Romer以內生成長模型研究結果顯示，知識累積將延緩資本邊際生產力之遞減，而資本累積將直接影響小國及開發中國家的總要素生產力之成長率。再根據兩部門成長模型(two-sectors model)，知識經濟已成為活絡工業先進國家經濟發展的動力。工業先進國家的經濟發展非但主導全球經濟的興衰，而且其經濟發展之經驗也是台灣乃至全球新興國家急欲學習借鏡的對象。本文擬針對美國、英國、日本、義大利、西德、法國、加拿大等七個工業先進國家之長期經濟發展按其經濟成長的速度及特性區分為：一、能源危機前較高成長期(1948-1972)；二、能源危機後較低成長期(1972-1995)；及三、知識經濟發展期(1995-2004)等三個階段，分別探討在不同階段中，影響其經濟發展之重要因素。至盼此研究結果能作為台灣及全球新興國家日後規劃長期經濟發展策略之參考。 The economic situation of industrial countries not only dictates the performance of global economy, their experiences often provide lessons to countries in Taiwan and other parts of the world. This paper delineates the long-run economic development of seven industrial developed nations, namely the United States, UK, Japan, Italy, (West) Germany, France and Canada, into three stages based on the pace of economic growth and characteristics: (1) higher growth stage (1948-1972); (2) lower growth stage after the energy crisis (1972-1995); and (3) knowledge-Based economy development stage (1995-2004), and attempts to explore the important factors that influence the economic development in respective stages. It is hoped that findings in this study may serve as reference to governments in Taiwan in the planning of long-term development strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".