L'utile et le juste de la discrimination dans la sélection, la classification et la tarification des risques assuranciels
Bibliographic record
Abstract
This thesis addresses the complex issue of risk classification in the field of insurance. Prior to accepting risks, insurance companies must first be able to evaluate those risks. Accordingly, they seek to collect the most information possible from, amongst other sources, the insured, so as to gage relative risk and evaluate whether to insure or not, to what degree and at what rate. In due course, the insurer will use this information on conjunction with statistical and actuarial calculations to draw hypotheses on the degree, probability and cost of risk. In selecting relevant risks for analysis, insurers will utilise set variables based on the area of insurance in which they operate. However, said variables are highly susceptible to being discriminatory. Notably, one thinks of sex and age which are contentiously considered by practitioners and scholars whether or not they operate in the field of insurance. This dissertation will examine exhaustively the normative framework in place in order to determine to what degree, if indeed at all, insurers can legitimately and legally utilize certain classifications such as age and sex in order to select, categorise and fix the price for the various risks offered to them.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".