Cross-cultural visual anthropology: Beyond repatriation, exploring indigenous and non-indigenous exchanges
Bibliographic record
Abstract
Collaboration is critical concept in Arctic anthropology, in which indigenous people participate not just as research subjects but as collaborative partners in advancing scientific knowledge. The trans-disciplinary approach develops new conceptual, theoretical, and methodological innovations that transcend discipline-specific boundaries. Such innovations facilitate engagement between indigenous and non-indigenous stakeholders in addressing real-world challenges. This paper documents several Siberian ethnography exhibitions organized by the authors and evaluates their anthropological and social significance. Historically, anthropological discourse has championed using visual materials as tools for cultural interventions aimed at societal transformation. Building upon this foundation, this study explores the challenges of both the Russian Arctic and Asian contexts. The article guides the readers to reconsider conventional anthropological perspectives and methods of collaborating both with the indigenous and non-indigenous partners. By outlining the authors experiences in involving local stakeholders across different countries in these exhibitions, they illuminate the impact of the exhibitions on diverse cultural contexts. As cross-cultural visual anthropology endeavors, the exhibits redefine the meaning of ethnographical snapshots as scientific knowledge and go beyond repatriating indigenous cultures or sharing research outcomes with the broader society. The cross-cultural exhibition is becoming a novel research modality and a tool for fostering social interactions.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".