Bibliographic record
Abstract
On May 19th this year, the central bank governor of Sri Lanka confirmed that the country could not repay its national debt due on April 18th in time (Jayasinghe & Pal, 2022). For the first time, Sri Lanka had defaulted on its sovereign debt since independence from Britain in 1948. It also announced its inability to continue paying for fuel (Jayasinghe & Pal, 2022). On July 6th, Sri Lanka declared national bankruptcy (Athas et al., 2022). This paper examined what led to Sri Lankan debt crisis and subsequent national bankruptcy and how the country could save itself from its situation. It analyzed secondary data from various published sources like news articles, journal articles, websites, and books. The study found that the country had high levels of external debt that outrun revenue. It also depends highly on imports to supply goods into the market. Its debt crisis was also influenced by economic shocks like the Russian-Ukraine war and the Covid-19 pandemic, which impaled production. Furthermore, the country's economic regulations were poor, making it unable to establish effective taxation, debt, and foreign reserve management systems. The company could improve its economic conditions by getting economic assistance from other countries and the IMF in the short term. Moreover, it also needs to restructure its foreign reserves and borrowings management systems.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".