Furs and Fabrics, Transformations, Clothing and Identity in East Greenland
Bibliographic record
Abstract
Today Arctic clothing is an important research topic. The first European researchers were\nfascinated by the Greenlander’s sophisticated technology, and they brought clothing,\nkayaks and hunting equipment back to Europe. Researchers still admire the quality of the\ntechniques used to produce a balanced material culture that was perfectly adapted to the\nArctic environment. Fur clothing was much better suited to meet the Arctic challenges\nthan the Europeans’ woolen garments. Nevertheless, Inuit clothing changed rapidly\nunder the influence of European culture. Nowadays, Greenlanders wear baseball caps,\nmilitary jackets and Nike shoes. Compared to the excellent hand-made fur clothing of the\nInuit, European mass-produced fabrics seem to represent a step backwards. Why did the\nEast Greenlanders break with the traditions of their ancestors? Why did they abandon\nmost of their perfectly adapted and beautiful fur clothing, and why did they adopt new\nstyles of dress? This book discusses the social implications of the changes in the clothing\nof Tunumiit (East Greenlanders)1 in relation to processes of social and cultural change in\nthe East Greenlandic societyDit proefschrift is uitgegeven in de reeksen: CNWS Publications en Mededelingen van het Rijksmuseum voor Volkenkunde ; no. 32 CNWS Publications en Mededelingen van het Rijksmuseum voor Volkenkunde no. 32
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".