Macro and Micro-Economic Policies in Financial Crises: Argentina 2000 and South Korea 1998
Bibliographic record
Abstract
Modern nations depend upon foreign investment to fund industrial developments in the nation and to fund government infrastructure developments. Governments use foreign investment to principally fund government services, when the tax base of the country has been insufficient to balance the government budget. But deficits can continue to grow, year after year. When the annual government budget deficit becomes a significant portion of the annual revenue of an economy (GNP), then foreign investors lose confidence in a government’s capability of continuing to finance its deficits. Investors stop buying government securities. The national currency exchange rate plunges. Local banks in a nation become insolvent. Bank runs occur as savers withdraw deposits from banks. Credit stops in a national economy, and businesses are unable to finance day-to-day production and pay wages. The economy plunges into a depression. Masses of people are unemployed. Property is lost. Families starve. Governments fall. The society descends into chaos. This occurred in the Asian Financial Crises, beginning in Thailand and spreading to other countries, including South Korea in 1997 and Argentina in 1999. Reviewing these cases, one can see that international financial institutions had neither correct economic models nor effective policies nor proper regulatory power -- to ensure that a global financial system was sound and also beneficial to economic growth in the nations of the world. And from this, one can see that IMF policies should have been instead focused not just on ‘macro-economics’ but also on the ‘micro-economics’ of each nation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".