Responsive Research in an Era of Reconciliation
Bibliographic record
Abstract
Dr. Jeff Corntassel is a writer, teacher and father from the Tsalagi (Cherokee) Nation and is Wolf Clan. He was the first to represent the Cherokee Nation as a delegate to the United Nations Working Group on Indigenous Peoples. He is editor of the collection, *Everyday Acts of Resurgence: People, Places, Practices* (Daykeeper Press, 2018). Jeff Corntassel received his Ph.D. from the University of Arizona and is currently Associate Professor at the University of Victoria and Associate Director of the Centre for Indigenous Research and Community-Led Engagement. His research and teaching interests focus on the intersection between sustainable self-determination, community resurgence, climate change and wellbeing. Dr. Jacqueline Quinless is a settler whose family origins are rooted to the communities of Secunderbhad and Hyderabad India. She works as Director of Research at Quintessential Research Group, which is a community, informed research practice specializing in environmental impacts, health and wellness research and gender-based analysis. Her forthcoming book is *Unsettling Conversations: Decolonizing Everyday Research Practices (University of Toronto Press) . This event will focus on the 94 recommendations of 2015 Truth and Reconciliation Commission (TRC) in Canada and the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), endorsed by the United States in 2010. In addition, how the relationship between Responsive Research, Indigenous nations and community partnerships can lead to more culturally informed socio-economic, health and environmental outcomes addressed. The event is sponsored by UAA Alaska Native Studies, the National Resource Center for Alaska Native Elders (NRC-ANE), and UAA Campus Bookstore.
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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 | 0.208 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.025 | 0.157 |
| Scholarly communication | 0.041 | 0.065 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".