The Principle of Proportionality: Unraveling the Practical Application of Proportionality in the EU Regulations and the Solvency II Directive for Insurance Undertakings
Bibliographic record
Abstract
Proportionality, pivotal to EU regulations and Solvency II, tailors rules to insurers’ size and complexity. Inconsistent application by supervisory authorities (NSAs) necessitates clarity to prevent undue costs. This study examines the issue via a review of the literature and industry discussions, emphasizing Solvency II’s introduction of proportionality and the varied interpretations it evokes. Transparent communication is crucial, and regulatory evolution must align with market dynamics, with the European Insurance and Occupational Pensions Authority (EIOPA) fostering convergence. Assessing proportionality mandates a comprehensive evaluation of an insurer’s nature, scale, and complexity. Regulatory distinctions between first-party and third-party risks could enhance market efficiency. Ultimately, a holistic, market-oriented approach is essential for proportionate regulation in the insurance sector, requiring concerted efforts to elucidate frameworks, foster transparency, and align regulatory evolution with market dynamics.
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 | 0.059 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".