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Record W2094463155 · doi:10.1093/bjc/azl066

The Institutionalization of Deceptive Sales in Life Insurance

2006· article· en· W2094463155 on OpenAlexaff
Richard V. Ericson, Aaron Doyle

Bibliographic record

VenueThe British Journal of Criminology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsKey person insuranceInstitutionalisationIndividualismLife insuranceSolidarityBusinessActuarial scienceMarketingInsurance policyEconomicsLawMarket economyPolitical science

Abstract

fetched live from OpenAlex

Interview and ethnographic data are used to show how deceptive sales practices are rife and institutionalized in the life insurance industry. The data are analysed using the concept of ‘moral risk’: the paradoxical tendency of the structure and culture of the insurance institution to facilitate and encourage risky behaviour on behalf of the various players in the insurance relationship, in this case behaviour by insurance companies and their agents that puts consumers at risk through deceptive selling. We give empirical evidence of five key sources of moral risk that are part of the enduring structure and culture of life insurance sales. Although moral risk has been pervasive in life insurance sales since the birth of the industry, it articulates with key contemporary social tendencies: the responsibilization of the individual consumer, the erosion of the social safety net, fragmentation, individualism and the attenuation of family ties, the growth of a ‘flexible’ labour force, and the downloading of regulatory responsibility from the state. Deceptive sales practices corrode trust and promote yet more individualism, erasing the potential of insurance as a mechanism of social solidarity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.032
GPT teacher head0.294
Teacher spread0.262 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

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