From the Smithsonian's MacFarlane Collection to Inuvialuit Living History
Bibliographic record
Abstract
Digital technologies are providing heritage institutions with a range of possibilities for sharing curatorial and ethnographic authority with communities of origin. In recent years, a number of digital projects have demonstrated the potential for museum digitization initiatives to connect tangible and intangible cultural collections to indigenous peoples – in particular, opening up discussions of the opportunities and challenges associated with “digital repatriation,” the return of heritage documentation in digital form to communities of origin 1 ( see figure 8 ). In these projects, technical experimentation and innovation intersect with diverse cultural contexts and protocols, research ethics, and approaches to ownership of cultural property. For example, the Mukurtu Content Management System and the Plateau Peoples’ Portal have demonstrated possibilities for integrating digital cultural objects into archives that respect and support existing cultural traditions and practices by replicating dynamic protocols for access and circulation of cultural knowledge. 2 These protocols, digitally encoded through long-term collaborative design and production, have challenged the default of open access in favor of local control over sensitive cultural heritage. 3 In another research collaboration between the Cambridge Museum of Archaeology and Anthropology and the A:shiwi A:wan Museum and Heritage Center of Zuni, digital collections were made available for reconnection to narrative and other forms of intangible knowledge, while demonstrating the extent to which institutional ideologies and practices had previously excluded Native American interpretations of their material culture. 4 GRASAC, the Great Lakes Alliance for the Study of Aboriginal Arts and Culture, was created with the goal of determining if it would be “possible to use information technology to digitally reunite Great Lakes heritage that is currently scattered across museums and archives in North America and Europe with Aboriginal community knowledge, memory, and perspectives,” 5 suggesting possibilities for the generation of new cultural knowledge by reuniting fragmented Aboriginal collections. My previous work with the Doig River First Nation in British Columbia on the virtual museum exhibit Dane Wajich – Dane-zaa Stories and Songs: Dreamers and the Land has shown that while the digitization and return of cultural documentation to communities of origin can facilitate self-representation and the articulation of local cultural property rights, digitization and circulation can make it virtually impossible to enforce those rights. 6
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 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".