Testing inter-observer error under a collaborative research framework for studying lithic shape variability
Bibliographic record
Abstract
Evaluating error that arises through the aggregation of data recorded by multiple observers is a key consideration in many metric and geometric morphometric analyses of stone tool shape. One of the most common approaches involves the convergence of observers for repeat trails on the same set of artefacts: however, this is logistically and financially challenging when collaborating internationally and/or at a large scale. We present and evaluate a unique alternative for testing inter-observer error, involving the development of 3D printed copies of a lithic reference collection for distribution among observers. With the aim of reducing error, clear protocols were developed for photographing and measuring the replicas, and inter-observer variability was assessed on the replicas in comparison with a corresponding data set recorded by a single observer. Our results demonstrate that, when the photography procedure is standardized and dimensions are clearly defined, the resulting metric and geometric morphometric data are minimally affected by inter-observer error, supporting this method as an effective solution for assessing error under collaborative research frameworks. Collaboration is becoming increasingly important within archaeological and anthropological sciences in order to increase the accessibility of samples, encourage dual-project development between foreign and local researchers and reduce the carbon footprint of collection-based research. This study offers a promising validation of a collaborative research design whereby researchers remotely work together to produce comparable data capturing lithic shape variability. Supplementary Information: The online version contains supplementary material available at 10.1007/s12520-022-01676-2.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".