Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
▀ The pattern of global credit risks looks very different today than in 2007. Risks are now mostly centred in China and emerging markets. “Excess” private debt in China is as high as $3 trillion compared with $1.7 trillion in the US a decade ago. Yet some pockets of significant risk still exist in advanced economies, which not only implies vulnerability to rising interest rates, but also that the scope for rate rises may be limited. ▀ With policy normalisation underway in the US and the scaling back of asset purchases expected to start soon in the Eurozone, we focus on assessing vulnerabilities across global credit markets. This article explores the topic using a top‐down, cross‐country approach. We find that although private debt and debt service ratios look more benign in advanced economies than a decade ago, they have deteriorated markedly in many emerging markets in recent years. ▀ Based on a measure of excess private debt – comparing private credit‐to‐GDP ratios with their trend – China, Hong Kong and Canada are the riskiest. When comparing debt service ratios relative to their long‐term averages, risks are also mainly concentrated in emerging countries. But Canada, Australia and some smaller European countries also have high debt service ratios that have failed to drop since 2007, despite the slump in global interest rates. ▀ Overall, aggregate private debt indicators look less worrying than in 2007. We would also argue that the concentration of excess private debt levels in China reduces the risk of a sudden financial crisis based on massive credit losses, such as the one in 2007–2010. But with corporate debt levels in the US, Canada and some other G7 countries above their long‐term trend, investors need to be attentive to these considerable pockets of risk.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it