The practical value of a life: priceless, or a CBA calculation?
Bibliographic record
Abstract
In a previous paper, we discussed that the application of cost benefit analysis (CBA) often incurs setting a value to a statistical human life (VOSL). This led to decades of research into what a reasonable value should be. These evaluations of the VOSL lead to widely varying results. Rather than attempting to harmonize on an average with large margins of uncertainty, the conclusion can be drawn that indeed there is no law of nature that determines what risk is acceptable and that, therefore, a consistent valuation of a human life cannot be expected. Nor can it be expected that there is a universally valid number for the acceptability of a risk. We argue that one should accept that standardization of acceptable risks has its practical limitations given by the – lack of – similarity in nature of the activity and the nature of the risk. In fact, attempts to force standardization are counterproductive. In many cases, one has to accept the only available alternative not involving violence, which is a political debate, terminated by the more general rule of law or constitution on how to settle such a debate and then accept the decision.
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 | 0.033 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".