Bibliographic record
Abstract
As the continent where humans evolved and thus exhibit the greatest genetic diversity, Africa is one of the most attractive places to conduct ancient DNA (aDNA) research. Yet the “aDNA revolution” only recently reached the continent, thanks in part to methodological breakthroughs that make it possible to extract aDNA from poorly preserved materials from hot and/or humid climates. Since the first fully sequenced ancient African human genome was published in 2015, dozens of additional genomes from the continent have illuminated population movements, economic and social transitions, patterns of adaptation, and the timing of our species' evolution. However, sequenced individuals come from archaeological contexts widely separated in space and time and represent only a tiny fraction of ancient human genetic diversity. Many questions and entire regions/time periods have yet to be explored using aDNA. This is also the case for non‐human African aDNA studies, which have been slower to develop in part because of poor preservation. This entry describes the science of aDNA, discusses how the field has revolutionized in the past decade, and explores the history of aDNA research in Africa starting with mummy studies in the 1980s. It concludes with a discussion of the ethical challenges facing African aDNA research, some of which are specific to the continent while others apply to postcolonial contexts more broadly. While there is major work ahead to ensure aDNA studies in Africa and beyond are conducted ethically and equitably, the field is poised to shift knowledge on the African past in the coming years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.101 | 0.059 |
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".