AN ACTUARIAL APPROACH TO ASSESSING PERSONAL INJURY COMPENSATIONS IN SINGAPORE: THEORY AND PRACTICE
Bibliographic record
Abstract
In Singapore personal injury litigations, successful claimants usually receive their compensations as a lump sum. The main advantage of a lump sum payment is that the proceedings can be concluded with a 'clean break' between the parties. The lump sum is a result of discounting the future pecuniary values into a single present-day amount, considering the time value of money and the claimant's mortality. Conventionally, lump sum awards are determined by making reference to a spread of amounts in comparable cases. However, a fairer method would be one that involves input from not only lawyers but also other experts including economists and actuaries. This study, which is carried out by an inter-professional working group, provides a set of actuarially computed tables for use in personal injury settlements in Singapore. The calculations involve a consideration of recent advancements in stochastic mortality modeling and an empirical study on the econometrics of real returns on risk-free assets in Singapore. We then present two recent personal injury cases in Singapore, aiming at helping the Singapore legal profession understand and use the economic principles with actuarial tables, and educating economists and actuaries the legal concerns and concepts in personal injury cases.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".