Bibliographic record
Abstract
Why do financial turbulence and crises occur? What are different types of financial crises? Why do different countries experience financial crises, while some are more resilient? These are intriguing questions that relate to financial turbulence and crisis. The financial system is inherently susceptible to turbulence and crises: The world has witnessed several rounds of financial turbulence since the early 2000s. The 2008 global financial crisis and the worldwide financial turbulence that took place following the impact of the COVID-19 pandemic are examples. Periods of financial turbulence relate to heightened uncertainty and volatility in financial markets, and some of those periods can trigger financial crises. It is puzzling that although some countries can weather financial turbulence without falling into a financial crisis, others do not. This was observed during the global financial crisis. For example, financial turbulence triggered a financial crisis in some of the liberal market economies such as the United States and the United Kingdom. In contrast, Australia and Canada remained relatively resistant to financial turbulence. The existing literature tends to justify how and why a period of financial turbulence resulted in a financial crisis by looking at individual structural-, institutional-, or actor-level factors. In addition to the independent (separate) effects of these three principal explanatory factors, there is a need for detecting and analyzing their individual; interactive; and/or cumulative structural, institutional, and agential explanatory factors at work. Thus, it is crucial to explore some of the interrelated dynamics informing agency behavior which generate socioeconomic outcomes. Specifically, we call for a rigorous and refined analysis of how and why <italic>complementarities</italic> and <italic>enabling conditions</italic> that stem from interactions between structural and institutional factors influence actors’ agency and socioeconomic/political outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".