The Potential and Pitfalls of Large Multi-Source Collections
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 theprovenance(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.
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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 | 0.161 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".