Normal Bodies, Normal Prices: Interdisciplinarity in Victorian Life Insurance
Bibliographic record
Abstract
Victorian life insurance offices challenged disciplinarity by inviting doctors and statisticians to work together toward a single aim, hence prompting these professions to depart from the territorialism that otherwise motivated them. They facilitated these links among disciplines by diverting the attention of their expert employees from specific pathologies to a common-enough conception of what counted as "normal" or "natural" in a field of knowledge. Nor did they do so in a way that replicated the reformist agendas of eugenics or social hygiene, which carried the perfectionist impulse of most late-Victorian disciplines into the interdisciplinary arena. Instead, they took the normal as they found it, because it was easier to make money that way than to try and convince people to aspire to a norm that did not yet exist. If interdisciplinarity in life insurance altered usual conceptions of what it meant to possess a normal body or to find one's place on a normal curve, it had a similar effect on the late-Victorian concept of a natural or normal price. Unlike classical and neoclassical economists, who identified cases of price disequilibrium (the economic equivalent of the pathological) and sought to remove obstacles that stood in the way of normal prices, Victorian life offices started with a normal price (their standard set of premiums for healthy-enough lives) and exclusively sold their product to customers who were normal enough to pay it.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".