The Potential and Pitfalls of Large Multi-Source Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Archaeologists’ newfound ability to access vast digital collections creates opportunities but also presents challenges when those collections are from varied sources, including public institutions and private collectors. We illustrate these challenges by comparing two analyses of gender in Mimbres pottery images. Both analyses used the same procedures, but one included material in private collections, while the second drew on a smaller but more controlled sample. Gender distinctions and division of labor were revealed by the first analysis, but the results were not duplicated in the reanalysis using the controlled sample. We consider reasons for the difference, addressing how collectors’ interests may skew collections and suggesting that some particularly desirable Mimbres pottery designs were created using modern paint. The article concludes with recommendations for how archaeologists can best use mixed collections. These include considering how collections might be skewed and designing analyses to counterbalance likely issues, more chemical analyses with representative samples to gauge the extent of modern manipulation of Mimbres vessels, collecting data on the provenance (i.e., collection history) of material in order to try to trace the likelihood of post-excavation modifications, and studying the process of collecting as a means of understanding the authenticity of artifacts.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it