Nunalleq, Stories from the Village of Our Ancestors: Co-designing a Multi-vocal Educational Resource Based on an Archaeological Excavation
Bibliographic record
Abstract
Abstract In 2017, the Nunalleq Project initiated the co-design of a digital educational resource for schoolchildren in the Yukon-Kuskokwim region that curates the story of the archaeological excavations in a way which engages with Yup’ik ways of knowing and traditional oral storytelling. Here, we present an account of an archaeological outreach project which creatively unites science and history with traditional knowledge and contemporary engagements. Co-creation of the Nunalleq educational resource revealed the diverse ways in which people connect to the past, sometimes expected, sometimes surprising. In particular, the project made space for a younger generation of Yup’ik who are forging new relationships with their heritage inspired by the archaeology from Nunalleq through traditional dance, art and shared experience. Ultimately, this article explores co-design as a means to illuminate the processes of interpretation from varied perspectives and worldviews with the aim of better understanding how the methods we use frame the knowledge we create.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".