Problems of measuring country's financial security. Journal of International Studies /
Bibliographic record
Abstract
The aim is to define the key issues ensuring financial security of a country. The studies proposes a technique to calculate a country's quality management index for a financial as a weighted average of the country's overall compliance with key international standards, rules and principles in the sector. The study uses statistical information from the Consultative Group to Assist the Poor, the World Bank Database and the Organization for Economic Co-operation and Development, to study financial security of the population in 142 countries with different levels of economic development. 47 variables, grouped into 10 aggregates, were included in the study: Disclosure of information upon opening a deposit account, Disclosure of information upon opening a credit account, Disclosure of general information, Periodic disclosure upon opening deposit accounts, Periodic disclosure Monitoring, Enforcing, Fair treatment, Recourses, Standards for complaints resolution. Analysis of the results show that the levels of financial security in the studied countries have a wide variation from the minimum to the maximum values. We see that among the countries with high levels of economic development, the leaders are Italy, Canada and Puerto Rico. Transition economies include Venezuela, Argentina and Mexico; among developing countries, we can mention Armenia, Azerbaijan and Uzbekistan.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".