Gleaning signals about the past from cemetery data
Bibliographic record
Abstract
Cemetery headstones provide an easily accessible source of demographic data in human populations. In common with other sources of demographic data, such as skeletal samples, cemetery data may not be representative of the populations from which they were derived. In some circumstances they can be reasonably representative, however, and in such cases they may provide signals about demographic changes in the population that contributed to the cemetery. We present here analyses of burials occurring between 1900 and 1990 at the Columbia Cemetery in Columbia, Missouri. Our analyses, in combination with archival materials relating to infrastructure improvements in Columbia and data on infectious disease mortality in the state of Missouri, show that patterns of death observed in the cemetery data provide evidence for the timing of changes in the health of Columbia's residents. At the time that major improvements in sanitation and hygiene were implemented, burials of individuals dying under age 45 decreased significantly while burials of individuals older than 45 remained relatively high. Furthermore, data on infectious disease mortality indicate significant declines in deaths from water- and milk-borne infections, but no change in mortality from respiratory illnesses. These data also indicate that observed changes occurred about a decade later in Columbia than in large cities and more densely populated states elsewhere in the United States. Thus, this study illustrates the value of cemetery data in helping to fill gaps about how and when different events known to affect patterns of birth and death may have played out across time and space.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".