Emerging Market Crises and the IMF: Rethinking the Role of the IMF in Light of Turkey's 2000–2001 Financial Crisis
Bibliographic record
Abstract
Recurring financial crises in the semi-periphery of the international economic system have raised serious questions concerning the role of the IMF in the era of financial globalization, particularly in the aftermath of the Asian Crisis of 1997. This paper attempts to provide a critical and, at the same time, balanced perspective on the Fund's involvement in crisis-ridden emerging markets, with special reference to the recent Turkish experience. The analysis points towards both the limitations underlying the Fund's approach and some of the dilemmas faced by the organization in trying to reform the economies of debtor countries, given the nature of the domestic political environment in those countries. It is also argued that the kinds of reforms promoted by the Fund are incomplete, insofar as they focus only on the regulatory role of the state, neglecting issues relating to income distribution and longer-term development. Two key conclusions follow: firstly, crisis-ridden countries need to develop a domestic political base to “internalize” the kind of reforms sponsored by the IMF, which are necessary to enable these countries to benefit from the process of globalization. Secondly, the countries concerned need to extend their horizons and develop their domestic capacities in areas such as income distribution and longer-term competitiveness, areas not traditionally emphasized by the Fund.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".