Bibliographic record
Abstract
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 complementarities and enabling conditions 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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".