Bibliographic record
Abstract
Abstract Trauma and violence in the past may be accessed in the present by studying the skeletons of deceased individuals. It may manifest, for example, as fractures or sharp force injury, marks in the skeleton that can be observed and interpreted after an individual’s death. Trauma in human bodies from archaeological and historical contexts has been of academic interest since the late 19th century, with Enlightenment forebearers, and this interest continues apace, as scholars incorporate social theory and clinical literature alongside bioarchaeological observations to better understand the context and effects of trauma on, within, and between individuals’ bodies. Determining what counts as trauma and recording it consistently, defining violence, incorporating context while avoiding overinterpretation, and appreciating the postmortem harm that has and continues to be done to individuals who persist in the present are all difficult threads that require ongoing reflexivity by researchers. Research on topics of trauma and violence is emotive work, demanding both consistency of quantifiable recording methods and deep empathy. Bioarchaeologists and paleopathologists study the skeletons of individuals from archaeological and historical sites. Interpreting patterns of trauma (e.g., injury type, number of injuries, stage of healing, distribution) observed within individual bodies and between groups allows researchers to investigate topics as varied as occupational health and safety, abuse and subjugation, sports and gender, human–animal relationships, warfare and resilience, disability and care, and even accidents and clumsiness. Such topics are intimately interwoven with considerations of identity, social race, gender, the life course, class, work, power, place of origin, and more. A broken bone is always more than just a broken bone.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".