“It Makes Me Feel Good to Teach People About My Culture:” On Collaborative Research Methods with Indigenous Young People
Bibliographic record
Abstract
In this article we, as settler scholars, explore process as method within a community-driven, supradisciplinary project in southern Alberta called Raising Spirit. The project was a collaboration between the University of Lethbridge’s Institute for Child and Youth Studies and Opokaa’sin Early Intervention Society, a nonprofit that serves Indigenous children and families in southern Alberta. The project team formed in response to Opokaa’sin’s need for a digital library of Blackfoot culture, language, and history. Here, we reflect on the methods used during this project, specifically paraethnography (Marcus & Holmes, 2008) and design studio (Rabinow, Marcus, Faubion, & Rees, 2008). Throughout, we argue that this approach produced a collective sphere (Rappaport, 2008) wherein young people and community partners, Indigenous and non-Indigenous, became collaborators throughout the process. In this space of vulnerability and potential, everyone could contribute, share, and learn.
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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 | 0.064 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.055 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".