Bibliographic record
Abstract
Abstract How archaeologists classify and categorize artifacts has the potential to direct and bias interpretations before analysis has taken place. A clear example of this phenomenon in arctic archaeology is the analysis of material culture classified as “art” attributed to premodern Tuniit peoples (Late Dorset Paleo-Inuit, ca. AD 500–1300). Often, analyses of Tuniit art pieces are restricted by the use of customary typologies that can impose modern assumptions of how Tuniit groups would have perceived their material culture. In this study, we address this problem by focusing not on the meaning embodied in the finished objects but on the identification of decision-making patterns of the object carvers and users as reflected through microscopic traces of manufacture and use. We argue that through such trace-focused observation, certain newly observed patterns may suggest greater diversity in decision-making processes (with regard to manufacture and use) than would be suggested by traditional typological grouping alone. This work has wide-ranging implications for how arctic archaeologists approach artifact classification and typological organization.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".