Linking age-at-death distributions and ancient population dynamics: a case study
Bibliographic record
Abstract
Introduction Skeletal series recovered by archaeologists frequently display patterns of death that differ from those of typical living, or historically documented, populations. Some of these prehistoric patterns are extremely widespread. Widespread patterns include notably small proportions of infants (see e.g., Acsádi and Nemeskéri 1970; Lovejoy et al . 1977; Buikstra et al . 1986), high proportions of older children and young adults (see e.g., Acsádi and Nemeskéri 1970; Weiss 1973; Lovejoy et al . 1977; Keckler 1997; Paine 1997), and an apparent excess of female death during the young adult years (Acsádi and Neméskeri 1970; Boldsen and Paine 1995, 1999). Most paleodemographers would agree that the small proportion of infants is primarily an issue of preservation and recovery (e.g., Buikstra et al . 1986). The other patterns have produced a wide range of explanations ranging from concerns about the archaeological process and paleodemographic methods (Bocquet-Appel and Masset 1982, 1996; Walker et al . 1988; Konigsberg and Frankenberg 1994; Konigsberg et al . 1997), to epidemiological ones (e.g., Lovejoy et al . 1977). Relationships between demographic characteristics of a living population and a skeletal series recovered from it are not straightforward (Sattenspiel and Harpending 1983; Wood et al . 1992; Paine 1997). If we wish to understand the demography of the living population, we must develop explicit, testable models (see e.g., Keckler 1997; Paine 1997, 2000) that clearly define relationships between the two (Howell 1982; Wood et al . 1992).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".