Bibliographic record
Abstract
The outbreak of COVID-19 has renewed interest in the familiar phenomenon of crises and cycles of distress in sovereign debt markets. Public debt, already at a perilous level before the pandemic, has since sharply increased. This is primarily the consequence of emergency expenditures undertaken by governments to protect their citizens and combat the public health crisis. According to the International Monetary Fund’s (IMF) Global Debt Database, borrowing rose by 28 per cent to comprise 256 per cent of Gross Domestic Product (GDP) in 2020, with governments accounting for half of this increase. In fact, the public debt is now at its highest level in almost six decades.1 With central banks tightening monetary policy, low debt service costs can no longer assuage concerns about the sovereign debt burden. Accordingly, defaults are expected to rise and debt restructurings are predicted to become more frequent. To date, post-COVID initiatives in the sovereign debt space have mostly focused on emergency-level solutions.2 The core response to the crisis, the G20 Debt Service Suspension Initiative (DSSI), has allowed a group of Lower Income Countries (LICs) to apply for a temporary deferral of debt payments to creditor countries.3 In parallel with this, the IMF also sought to alleviate the pandemic’s impact on struggling economies by granting short-term debt relief and providing emergency liquidity assistance, particularly through new allocations of Special Drawing Rights (SDRs).4 Beyond these immediate solutions, the pandemic has also catalysed an array of reform proposals ranging from improved transparency of sovereign debt statistics to multilateral debt buyback programmes and changes in major governing laws of sovereign debt transactions.5 Among these suggestions has been the issuance of state-contingent debt, ie debt instruments linked to external variables such as exports, commodity prices and GDP. Researchers at the US Federal Reserve Banks, for example, propose GDP-linked bonds as a way to reduce defaults and help debtors return to viability and growth.6 In a similar fashion, a recent IMF staff paper has posited that the uncertainty following COVID-19 presents an opportunity to explore GDP-linked bonds, with payments adjusted to the borrower country’s economic upsides and downsides.7 Griffith-Jones and Sharma offer a simple example of how such an instrument would work. Assume a country with a trend growth rate of 3 per cent per year can borrow on the market—and on a plain vanilla bond—at 7 per cent interest a year.8 Instead of borrowing money on plain vanilla terms, the country can instead issue bonds that promise 1 per cent above or below 7 per cent for every year that the economy’s growth rate goes beyond or falls short of 3 per cent.9
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 | 0.023 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".