Connectivity and closeness among international financial institutions: a network theory perspective
Bibliographic record
Abstract
This article focuses on connectivity and closeness between financial institutions. Financial institutions are a subset of multinational corporations and play an important role in our modern economies. By studying connectivity and closeness, this article proposes a network theory approach to the notion of systemic risk. Using network theory, we propose to look at potential networks between financial institutions through their boards of directors. Measures of centrality (degree, closeness, betweenness, eigenvalue) and force-directed networks are provided for each country. We built a large sample (43,399 individuals; 2,209 institutions) across 52 countries using Bureau van Dijk's database. We find corporate interlocks showing - to some degree - the level of concentration within the financial system. The main contribution of this article is to show some evidence of small-world properties of the international financial system; the ramifications of this question could be critical, notably in terms of systemic risk.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".