Bibliographic record
Abstract
The Dorset cultures lived in the Canadian Arctic and Greenland around two thousand years ago (800 BC – AD 1300). Despite their relatively long time period and large geographic span, it has always been noted that collections of Dorset artefacts are “remarkably uniform” in contrast to other Arctic cultures. This has then led to further theoretical assumptions, for example that there was a high level of information and object exchange across the entire Dorset span. However, this is because Arctic archaeologists have mainly classified the objects based on superficial characteristics such as shape, style, and form (otherwise known as the typology). Microwear analysis is a scientific method that allows archaeologists to see microscopic traces showing how objects were made and used. By using this method, we can understand not just the finished objects themselves (which is the main focus of methods which look only at typology), but also the people who interacted with them. For example, we can learn about the choices made during the steps of manufacture, or the various ways in which different people used the same object type. This PhD project therefore challenges the assumption of “uniformity” of Dorset artefacts by using microwear analysis to look at them in new ways. Using this method, we can then gain further insight into how Dorset groups interacted with their material world.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.040 |
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".