Bibliographic record
Abstract
Learning Objectives In our day-to-day life, we face many risks. We can get sick or injured. We may get into an accident while we are driving to work. A fire or a flood can damage our houses. If that does not happen, someone may break into our houses and steal our belongings. In addition, we face the risk from not knowing exactly how long we will live. We may live shorter or longer than expected. If the former, our dependents may suffer from lack of financial support. If the latter, we may run out of retirement money. The list goes on. When these unpleasant events happen, their negative effects on our physical and/or financial well-being can be substantial. Traditionally, there are insurance products that people can buy to manage these risks. In this chapter, we discuss the theoretical foundation of insurance. We look at insurance from both the buyers' and sellers' perspectives. Specifically, we want to determine when insurance should be used and how it is priced. In the process, we formalize the concepts of risks and people's attitudes toward them. The understanding from this chapter will be helpful to our discussion of life insurance in the next chapter. The Concepts of Risk and Risk Preferences Definition of Risk Although there is no single, universally accepted definition of risk , a common one defines it as the variation in possible outcomes of an event that is subject to chance. The greater the variation, the more risky the event is. Risk can be classified into two distinct categories – speculative risk and pure risk.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".