Bibliographic record
Abstract
In this paper, we use a rich dataset of several countries to analyze how sound political measures affect cross-border bank flows. Furthermore, our work is the first to comprehensively examine various components of political stability on the aforementioned subject using a larger sample than previous studies, and covering the period 1984–2013. Our paper will inform policy makers which particular aspects of political stability have a significant effect on cross-border bank flows and provide an outline on the favorable long term political and institutional development to increase such flows. We find that sound political measures—and therefore, higher political stability—increase cross-border bank flows, especially in advanced economies. Moreover, we find that in advanced economies, the political stability components; socioeconomic conditions, investment profile, corruption within the political system, religious tensions, ethnic tensions, and bureaucracy quality have a positive and close association with such bank flows. In our work, we also find that policies aiming to increase political stability have a stronger impact after the financial crisis of 2008, namely with regard to policies that affect socioeconomic conditions, investment profile, corruption within the political system and religious tensions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".