Different Measures of Country Risk: An Application to European Countries
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
Country Risk (CR) is a relevant instrument to analyze and understand economic performances and relationships between different countries in the actual economic and political international globalized context. The present work develops indexes for the European Union countries by applying three different methods in the field of formative approach. Our aim is to show how robust CR measurements can be developed by operational and easily computable methods. We identify a set of significant variables included in the reference literature. Then, we propose three simple aggregative processes in order to obtain CR measures, at a precise time and over time. As a result, if we compare the outcomes, similar CR rankings emerge. In other words, there are no relevant differences in results also due to different methods of applications. The findings demonstrate that the choice of the aggregation method depends on the willingness of the researcher to baste the analysis with or without weighing and, therefore, on the semantic content that is assigned to the entire research structure. Each analysis should follow a disinterested theoretical–methodological consistency, knowing that the choice of a particular indexing process in the field of aggregation does not significantly alter the nature of the results compared to what would result by applying a different method.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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