Collaborative Research in Academic Archaeology: A Perspective from the Yukon-Alaska Borderlands
Bibliographic record
Abstract
Archaeological investigations among the Upper Tanana speaking peoples of the Yukon-Alaska Borderlands began with seemingly conventional approaches to respectful consultation and collaboration in the early 1990s. After having been engaged with this work since 2011, first as a student and now as a researcher at the Little John site, I have accumulated experiences which have shaped my ideas regarding the maintenance of collaboration, how it evolves over time, and how its unique community context promotes a relaxed and mixed methodological strategy that I call a destandardized approach to collaboration. This includes formal and informal relationships or friendships, diversifying institutionalized concepts of capacity building, and deprioritizing disciplinary goals that impose time constraints in favour of just being present. This approach has been nurtured by the cultural ethos of our host community over twenty-five years of engagement and an ongoing conversation of “how” anthropologists should approach and practice community collaboration. The result is an academic archaeological program which has become integrated into a small northern community of transitional hunter-gatherers that contribute to the shared goals of collaborative archaeologies—particularly the deconstruction of power resulting from colonial legacies and the reconstruction of power rooted in locality.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.070 | 0.044 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".