Bibliographic record
Abstract
Abstract Some of the developing (non‐US) high‐yield markets were briefly mentioned in the previous chapter, but before the development of the non‐US markets for high‐yield bonds is addressed in this chapter, the countries from which national and corporate borrowers were driven to the US markets for capital are reviewed. They represent the full spectrum of size, location, and creditworthiness, and include Argentina and Brazil (which accounted for nearly 90% of all high‐yield bonds issued in the USA by South American Corporations in the 1990s), Canada, Australia, and the UK. The next section of the chapter reviews the development of the European high‐yield market, the expansion of which peaked in 1998 but then paused (and showed a drop in returns) following the Russian crisis. Data are given contrasting the different European financial systems, and difficulties arising from the different European insolvency regimes and transnational bankruptcies are discussed. The last part of the chapter discusses the development of the Canadian high‐yield market, and the attempts made to break through into this market in Asia, which are limited by cultural taboos against bankruptcy (data are given on the different creditor rights in eight Asian countries and four South American countries).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".