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Record W2319959292 · doi:10.7202/1106943ar

Determinants of Total Compensation forAuto Bodily Injury Liability Under No-Fault:Investigation, Negotiationand the Suspicion of Fraud

2023· article· en· W2319959292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAssurances et gestion des risques · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCompensation (psychology)NegotiationLiabilityFault (geology)PsychologyComputer securityBusinessActuarial scienceComputer scienceSocial psychologyPolitical scienceLawAccountingSeismology

Abstract

fetched live from OpenAlex

Auto Bodily Injury Liability claim payments are predominantly negotiated settlements, with less than two percent the result of complete litigation and jury trials. All settlements consist of a combination of claimed economic loss, called special damages, and a payment for “pain and suffering”, called general damages. The dependence of the total compensation on a variety of factors relating to the type and magnitudes of the economic losses, medical and wage loss, and to the type and severity of injury has been explored by prior researchers who found medical losses to be the primary determinant of total compensation but they also found that other severity variables play a distinct and significant role in the final settlement values. Further research introduced the notion that both the information gathered in the course of investigation and the adjuster’s attitude toward the quality of the claim, especially the suspicion of fraud, also played a significant role in the final settlement value. Recently, it has been shown that settlement values for subjective injury claims are systematically lower relative to special damages and indicate that insurers use their negotiating power to obtain lower settlements on questionable claims as a rational response to the presence of fraud and build up claims. The current paper extends that research by examining additional variables specifically related to the investigation and negotiation processes and quantifying the effect of those variables on the final total compensation. In particular, we find that strain and sprain claims command lower general damages relative to specials, even in the absence of suspicion of fraud and build up, but that the intensity of suspicion of fraud and build up can reduce overall payments as much as 24 percent. For the first time, the negotiating effect of attorney demands enters the quantitative model in addition to the usual contingency fee. Finally, evidence that insurers are isolating low impact collisions and reducing the compensation through negotiation is explored and quantified.

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.

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)0.002
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 score0.073
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.046
GPT teacher head0.266
Teacher spread0.219 · 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