Future Directions and New Approaches to Study Ancient Populations
Bibliographic record
Abstract
Abstract The last chapter of the book is an invited contribution exploring the potential of new methods and datasets, such as aDNA and paleoanthropological studies, in understanding ancient demography, migratory processes, and health in the Iberian peninsula. While these fields are still young in Spain and Portugal, they are expected to develop in the near future and provide alternative data that may challenge or complement our present views. Genetic studies using ancient material make possible obtaining large amounts of data about population, family relationships, kinship, and the movement of individuals. Similarly, the study of health, pathologies, and pandemics has started to merge with the study of ancient demography, and osteoarchaeological studies can provide an alternative view of general demographic characteristics. Chapter 8 integrates palaeodemographic, anthropometric, palaeopathological, and palaeodietary data from numerous burials and some necropoleis in the province under study showing some initial results and the potential these types of studies will have to understand ancient demography in the future.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".