Human biologists in the archives: demography, health, nutrition and genetics in historical populations
Bibliographic record
Abstract
Introduction A few years ago we began a conversation about how the use of archival data in biological anthropology was changing. Whereas in the past archival research was conducted by a very small number of us (see Foreword, this volume), that number seemed to be growing steadily. In addition, questions, themes, and approaches appeared to be emerging that gave archival research its own character and rationale. Rather than simply being an adjunct, less desirable alternative, or even an afterthought to field or laboratory work, archival research had become the approach of choice for some researchers and some questions. Today, a significant number of physical anthropologists engage in research on populations whose data come primarily from the historical record. In addition, distinctive approaches have emerged from these investigations that can be differentiated from the work of colleagues in, for example, historical demography or the history of medicine (see chapter 14). We also observed that there has been little opportunity to assemble a body of this work or to reflect on its common themes or its contributions to theory and method in physical anthropology. The purpose of this volume is to provide a forum in which researchers who are actively engaged in historical projects present their current research and at the same time more explicitly consider its place in physical anthropological theory and method. The articles in this volume are new contributions to the physical anthropology literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".