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Record W2920931937 · doi:10.6000/1929-7092.2019.08.23

Insurance of the Termination Risk of Projects with Joint Companion Activity

2018· article· en· W2920931937 on OpenAlexvenueno aff
I. V. Sukhorukova, N. A. Chistyakova

Bibliographic record

VenueJournal of Reviews on Global Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSustainable Finance and Green Bonds
Canadian institutionsnot available
Fundersnot available
KeywordsJoint (building)BusinessActuarial scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Actuarial calculations are based on the study of mathematical models of financial schemes in insurance taking into account the stochastic nature of insurable events. They lie at the intersection of mathematical and economic disciplines. This paper is devoted to the formulation and investigation of the mathematical model of insurance of a joint project of partners from the risk of its early termination due to the retirement of one of the companions due to external circumstances. In case of an insured event, the partner remaining in the project receives insurance to continue the project. In order to assess the obligations of the insurance company, an economic-mathematical model of personal joint insurance of participants has been constructed to calculate the necessary characteristics of such an agreement. To describe the dynamically changing threats to terminate the insurance contract, the concepts of the intensity of retirement functions of insured persons are introduced, which are convenient in the study of insurance contracts with several participants. Possibilities of payment of insurance compensation to each partner are received and probability of that the insurance company should pay insurance compensation are received. Calculations are made using the example of specific functions of the intensity of retirement of partners. Practical recommendations are given related to the use of numerical methods or simulation methods in calculations based on the obtained universal formulas.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.058
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.240
Teacher spread0.202 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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