Dependent financialisation and its crisis: the case of Turkey
Bibliographic record
Abstract
Abstract Although the financialisation research agenda has developed rapidly, especially since the Great Recession, there are still some gaps in the literature regarding Emerging Capitalist Countries’ (ECCs) financialisation experiences. We argue that the concept of dependent financialisation applies more appropriately to ECCs, in line with a recently burgeoning heterodox literature on ECC financialisation. More specifically, we suggest that a critical rethinking of the Dependency School’s arguments in the light of the current uneven and combined financialisation experiences of ECCs will generate a better structured theoretical framework. To support this, we take Turkey’s financialisation experience and its 2018–19 crisis as a case study. We suggest that dependent financialisation in Turkey can be grasped in terms of the emergence of an economic structure with two specific characteristics: (i) it has structurally higher interest rates and a high dollarisation rate due to Turkey’s mode of integration into the hierarchical global financial system; (ii) economic activity increasingly depends on capital inflows, which make the country’s economy vulnerable to the negative effects of the monetary policy decisions of major central banks. Finally, we argue that recurrent crises are the manifestations of the limits of dependent financialisation, which generates an unsustainable mode of integration to the global economy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".