<scp>A Test of the Eclectic Paradigm: Evidence From the U.S. Reinsurance Market</scp>
Bibliographic record
Abstract
Abstract This study provides a test of the eclectic paradigm with data from U.S. reinsurers. The U.S. reinsurance industry provides a unique setting to test the eclectic paradigm due to the extensive data available on U.S. reinsurers and the well‐developed literature related to reinsurance. The ability to test the hypotheses related to the eclectic paradigm in a service industry and incorporate industry‐specific factors adds to the eclectic paradigm literature which has traditionally focused primarily on manufacturing firms. In addition, the application of the eclectic paradigm to the reinsurance industry provides an empirical framework that combines several prior streams of literature which examine the reinsurer's decision to internationalize. The current study includes firm‐specific factors, country‐specific factors of the international markets, and factors related to the U.S. reinsurance industry. This article finds support for traditional factors impacting globalization such as host market size, loss experience, and competitiveness as well as reinsurer's ability to expand based on available capacity. Understanding the importance of firm‐, country‐, and industry‐specific factors is key for managers, as analyzing these issues in isolation may lead to an incomplete picture of the factors impacting the internationalization decision, hindering managers' ability to make decisions that are in the best interest of the firm. With the continued interdependence of the world reinsurance marketplace, as well as the recent expansion of the European Union, internationalization issues are of critical importance not only to U.S. insurers, reinsurers, and regulators, but also to their global counterparts.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".