Accessibility and Educational Standards in Human Osteology through Digital Learning Practices
Bibliographic record
Abstract
This article explores the growing use of digital tools in teaching human osteology, highlighting the need for standardized educational practices in biological anthropology and proposing ways to enhance accessibility and ethical guidelines through digital pedagogy. Digital tools are becoming more prominent in the teaching and education of human skeletal remains. Currently, no standards exist regarding educational practices utilizing digital pedagogical tools in biological anthropology. To better understand current practices of digital pedagogy for human osteology, a survey was distributed to professionals and students in biological anthropology inquiring about teaching methods in osteology-related courses. The goal of this survey was to gauge current applications of digital osteological pedagogy to serve as a foundation to initiate conversations around standard digital education in biological anthropology. The results indicate that, while most educators have access to physical remains, almost half of the survey participants incorporate digital tools in teaching. Additionally, we found that respondents almost unanimously believe digital tools should be included in our educational practices and that digital learning can improve accessibility to education. Incorporating pedagogical theories and current virtual anthropology research, we propose ways to move education and training in biological anthropology forward through digital pedagogical tools, training, and collections while promoting both standard educational and ethical practices.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".