Snapshots of Digital Scholarship in Zooarchaeology: Introduction to the Special Issue
Bibliographic record
Abstract
Special Issue on Digital Zooarchaeologysharing landscape include approaches to data access, aggregation, and preservation (e.g., DINAA 1 , tDAR 2 , ADS 3 , CARD 4 , SPARC 5 ); and refinements in the application of digital methods (geometric morphometrics, 3D modeling, GIS).Many parallel initiatives undoubtedly exist in ethnobiology often on more localized community focused scales.This special issue features the work of a diverse group of researchers employing digital techniques in zooarchaeology (a.k.a.archaeozoology).Zooarchaeology is the study of animal remains in the archaeological record (e.g., bones, teeth, shells, antlers, horns, and similar tissues, as well as biomolecular remains, such as proteins and ancient DNA).The study of these remains informs understanding of past human activities and human influenced environments.Seven papers by eighteen authors from Europe and North America showcase digital research spanning three continents that includes research on fish, mammals, and birds as well as an introspective examination of zooarchaeologists themselves.These papers emerged from a symposium at the International Council for Archaeozoology conference, which took place at the Museo de Historia Natural de San Rafael in Mendoza, Argentina in September of 2014.This gathering of the global community of zooarchaeologists offered a chance to showcase new techniques and technologies that address a variety of key research questions. Improved Data SharingA prominent theme explored in this special issue is the improvement of data sharing across zooarchaeological research settings and digital platforms.While 'big-data' approaches aim to standardize large-scale datasets, archaeologists are also realizing the im-"Anthropology begins with people and ends with people, but in between there is plenty of room for computers" -A quote attributed to Claude Lévi-Strauss by Eric Wolf (1964:52).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".