The Nooter photo collection and the Roots2Share project of museums in Greenland and the Netherlands
Bibliographic record
Abstract
In 2008 two Dutch museums and two Greenland museums started a cooperative venture to share the photo collections of museums in the Netherlands. The photographs were taken from 1965 to 1986 by husband and wife Gerti and Noortje Nooter in Diilerilaaq, a village in the Sermilik Fjord (East Greenland). Gerti Nooter, then curator at the Museon in The Hague and at the National Museum of Ethnology in Leiden, was doing fieldwork in that changing hunting community and, as part of that research, took photographs and collected museum objects for both Dutch museums. The National Museum of Ethnology in particular has long had a working relationship with Greenland museums and the local Tunumiit community. Through the visual repatriation project Roots2Share, these photographs have been scanned and returned to the communities where they originated and where they can now be accessed locally. As a product of cross-cultural interactions, they depict ancestors of present-day Tunumiit and carry multiple meanings: ethnological or exotic ones for a Dutch public and historical or ancestral ones for the people of Diilerilaaq. Many stories have been told about them. This article explores the relationship between the photographs and Tunumiit knowledge, as well as issues of cultural heritage, ownership, and sharing of these images.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.003 |
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".