Using the life history model to set the stage(s) of growth and senescence in bioarchaeology and paleodemography
Bibliographic record
Abstract
Paleodemography, the study of demographic parameters of past human populations, relies on assumptions including biological uniformitarianism, stationary populations, and the ability to determine point age estimates from skeletal material. These assumptions have been widely criticized in the literature and various solutions have been proposed. The majority of these solutions rely on statistical modeling, and have not seen widespread application. Most bioarchaeologists recognize that our ability to assess chronological age is inherently limited, and have instead resorted to large, qualitative, age categories. However, there has been little attempt in the literature to systematize and define the stages of development and ageing used in bioarchaeology. We propose that stages should be based in the human life history pattern, and their skeletal markers should have easily defined and clear endpoints. In addition to a standard five-stage developmental model based on the human life history pattern, current among human biologists, we suggest divisions within the adult stage that recognize the specific nature of skeletal samples. We therefore propose the following eight stages recognizable in human skeletal development and senescence: infancy, early childhood, late childhood, adolescence, young adulthood, full adulthood, mature adulthood, and senile adulthood. Striving toward a better prediction of chronological ages will remain important and could eventually help us understand to what extent past societies differed in the timing of these life stages. Furthermore, paleodemographers should try to develop methods that rely on the type of age information accessible from the skeletal material, which uses life stages, rather than point age estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.169 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".