Cross-Cultural Digital Storywork: A Framework for Engagement with/in Indigenous Communities
Bibliographic record
Abstract
While Indigenous peoples have long urged attention to Six Rs (respect, relevance, reciprocity, responsibility, relationality, and representation) that are important to community-engaged work, application of these principles has been sporadic within the filmmaking industry. Many Indigenous communities do not have the technical expertise and/or resources needed to support professional quality audiovisual production. As a result, they rely on predominantly White filmmakers from beyond the community. Unfortunately, mainstream filmmaking practices have historically demonstrated a disregard for Indigenous ways of knowing, and a scarcity of meaningful relationships between filmmakers and community members has further contributed to a legacy of insensitive filmmaking within Indigenous contexts. In addition, internet-based distribution of cultural content raises questions about post-production sovereignty. In this project, Tribal College (TC) students and faculty partnered with students and faculty from a Predominantly White Institution (PWI) to develop culturally sustaining and revitalizing documentaries using storywork, digital storytelling, ethnocinema, and community-centered participatory research. Throughout the Digital Histories Project, TC participants gained technical expertise, PWI participants learned about culturally sustaining/revitalizing filmmaking, and faculty leaders identified ways to support use of the Six Rs within social science, history, and teacher education. Results offer methodological and pedagogical insights for scholars, educators, tribal leaders, and filmmakers.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".