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Record W7113901295 · doi:10.52843/cassyni.g78kyf

The Role of Relief Payments and Premium Subsidies in Optimal Insurance Contracting

2025· article· W7113901295 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Language
FieldDecision Sciences
TopicRisk and Portfolio Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSubsidyPaymentBridging (networking)Multidisciplinary approachRisk premiumRisk managementInsurance policyPayment by Results

Abstract

fetched live from OpenAlex

[***Risk Sciences***](https://www.keaipublishing.com/en/journals/risk-sciences/) aims to foster diverse, multidisciplinary insights in risk sciences, bridging theory and practice through a series of academic activities. Dr. Jiang's research interests mainly lie in Design of optimal (re)insurance, Risk measures, Clustering of financial assets and Dependence structures. This speech record is an excerpt from Dr. Jiang's keynote speech for the [Risk Sciences Colloquium – 2025 International Workshop on Risk Sharing (IWRS)](https://www.keaipublishing.com/en/journals/risk-sciences/event/2025-international-workshop-on-risk-sharing-iwrs/) , which was held on July 6-7, 2025, in Beijing, China. This video is categorized under ***Catastrophe Risk Sharing***.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.316
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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