Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Abstract</h3> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it