The Inuvialuit Living History Project: Digital Return as the Forging of Relationships Between Institutions, People, and Data
Bibliographic record
Abstract
Digital return is described in this paper as a process of creating and maintaining relationships between heritage and cultural institutions, people, and digital data. Our project reflects a rapidly shifting technological context in which the creation of access for originating communities to their heritage in distant museum collections and the collaborative multimedia production are increasingly parallel projects. In 2009, a delegation of Inuvialuit Elders, youth, seamstresses, and cultural experts from the Inuvialuit Settlement Region in the north traveled with a group of anthropologists, archaeologists, educators, and media producers from the south to research and document the Smithsonian’s MacFarlane Collection. In the years following this initial visit, the project team collaboratively developed a virtual exhibit and community-based digital archive called “Inuvialuit Pitqusiit Inuuniarutait: Inuvialuit Living History.” This project features the digital MacFarlane Collection, documents the delegation’s visit to the Smithsonian, and connects contemporary Inuvialuit interpretations of the collection to ongoing cultural practices in Inuvialuit communities. Through the lens of this virtual exhibit, we explore central issues of access to Aboriginal cultural heritage, ownership of digital heritage, and new forms of collaboration between holding institutions and Aboriginal communities that digital practices are facilitating. We demonstrate how new digital networks connecting heritage institutions and their data are creating opportunities for Aboriginal recontextualization of heritage, while presenting significant challenges for the long-term preservation of digital materials.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".