International Capital Flows
Bibliographic record
Abstract
Cross-border capital flows worldwide have risen substantially since the mid-1970s, from US$1.2 trillion in 1980 to $5.8 trillion in 2004. The pace of the growth (at an average annual rate of 6.6 per cent) surpasses by a big margin those of the world GDP (at 1.7 per cent per annum) and the world exports (at 3.1 per cent per annum). Developed economies are the most important source countries, accounting for 92 per cent of the aggregate outward capital flows in 2004. They are also the most important recipients, accounting for 91 per cent of the aggregate inward capital flows in 2004. A small number of developing countries — commonly known as emerging market economies — receive the lion’s share (nearly 70 per cent) of the remaining international capital flows in 2004. More than 130 other developing economies are more or less bypassed by the surge in the capital flows. (For these calculations, developed countries consist of the following 25 countries: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Euro Area, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States. Emerging market economies consist of the following 22 economies: Argentina, Brazil, Chile, China, Colombia, Egypt, Hong Kong SAR, Indonesia, India, Israel, Korea, Morocco, Mexico, Malaysia, Pakistan, Peru, Philippines, Singapore, Thailand, Turkey, Venezuela, and South Africa.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.054 |
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".