Revolutionary Fossils, Ancient Biomolecules, and Reflections in Ethics and Decolonization: Paleoanthropology in 2019
Bibliographic record
Abstract
ABSTRACT Over the past few decades, paleoanthropology has undergone a transformative shift away from studies focused solely on traditional assessments of skeletal anatomy. Prior to this shift, a review highlighting a year of research may have primarily consisted of a description of new fossil discoveries; in 2019, however, this review also incorporates novel subject matters such as ancient DNA, paleoproteomics, and studies applying a wide array of new analytical methods and theoretical frameworks to paleoanthropological questions. Through these new advances, the nonlinearity and complexity of hominin evolution has been illuminated, emphasizing the importance of interdisciplinary work in progressing the field. In addition, similar to the broader discipline of biological anthropology, the topic of decolonization has been reflected on and discussed. Further, researchers in paleoanthropology are grappling with important issues related to open access and data sharing. In light of this widening scope, this review centers on a collection of studies that focus on five key themes: (1) new discoveries; (2) data sharing and ethics; (3) human origins research; (4) paleogenomics, and new advancements in paleoproteomics; and (5) introspection on a colonial history. Paleoanthropology is coming of age, and in 2019 especially, published research has been reflective of this. [Evolutionary anthropology; human evolution; year in review; human origins]
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".