Aggregates, Formational Emergence, and the Focus on Practice in Stone Artifact Archaeology
Bibliographic record
Abstract
Abstract The stone artifact record has been one of the major grounds for investigating our evolution. With the predominant focus on their morphological attributes and technological aspects of manufacture, stone artifacts and their assemblages have been analyzed as explicit measures of past behaviors, adaptations, and population histories. This analytical focus on technological and morphological appearance is one of the characteristics of the conventional approach for constructing inferences from this record. An equally persistent routine involves ascribing the emerged patterns and variability within the archaeological deposits directly to long-term central tendencies in human actions and cultural transmission. Here we re-evaluate this conventional approach. By invoking some of the known concerns and concepts about the formation of archaeological record, we introduce notions of aggregates and formational emergence to expand on the understanding of how artifacts accumulate, what these accumulations represent, and how the patterns and variability among them emerge. To infer behavior that could inform on past lifeways, we further promote a shift in the focus of analysis from the technological and morphological appearance of artifacts and assemblages to the practice of stone use. We argue for a more rigorous and multi-level inferential procedure in modeling behavioral adaptation and evolution.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".