CHEMISTRY VERSUS DATA DISPERSION: IS THERE A BETTER WAY TO ASSESS AND INTERPRET ARCHAEOMETRIC DATA?
Bibliographic record
Abstract
Given the common use of chemical concentration data to define ceramic groups that aid in the exploration of ancient technology, trade and provenance, it is important to reflect on how we collectively establish and define both chemical groups and outliers. In this paper, we argue that commonly used data analysis procedures, such as principal component analysis and centred log‐ratio principal component analysis favoured in the examination of ceramic chemical data, although rapid and easy, may overlook existing chemical groups and outliers, especially when the ratio of non‐diagnostic to diagnostic elements is high. To evaluate whether geochemistry is more important than data dispersion in data assessment, we re‐examine chemical concentration data from previously published ceramic, clay and daub samples from the lower Ohio River Valley. We begin by briefly discussing steps we took to ensure that the data set reflects geochemical differences, rather than analytical or data transfer errors. Next, we use bivariate plots, as well as PCA and CLR–PCA, to examine different versions of our altered data, using varying numbers of element combinations. We propose that the careful examination of bivariate plots is critical in establishing the elements that should be included in PCA and other multivariate analyses.
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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 | 0.169 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".