Forensic Demography: An Overlooked Area of Practice among Applied Demographers
Bibliographic record
Abstract
The term "forensic economics" is widely recognized within civil legal circles in the United States, and is usually understood to involve the calculation of damages from personal injury, death, and employment loss.Forensic demography is a term that is not widely recognized because it has only been used in a handful of civil rights and related cases.In this paper, we argue that applied demographers have skills that apply to civil cases involving the calculation of damages from personal injury, death, and employment loss.We do this by identifying the equivalencies and similarities in core measures and concepts, albeit with different names, used by forensic economists and applied demographers.We also illustrate these commonalities with a hypothetical case assessing the present value of damages in a loss of life civil case and discuss some challenges facing an applied demographer who would like to move into the field of forensic demography.We conclude with the observation that applied demographers are well equipped to extend forensic demography into civil cases of this nature.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".