ȻENTOL TŦE TEṈEW (TOGETHER WITH THE LAND)
Bibliographic record
Abstract
In this paper, Part 2 of a two-paper series, we extend our learning on land- and water-based pedagogies from Part 1 to outline broader debates about upholding resurgence in frontline practice with Indigenous children, youth, and families. This article shares key learning from an Indigenous land- and water-based institute held from 2019 to 2020 that was facilitated by knowledge keepers from local First Nations and coordinated by faculty mentors from the School of Child and Youth Care at the University of Victoria. The purpose of the one-year institute was to convene a circle of Indigenous graduate students and faculty to engage in land- and water-based learning and meaningful mentoring connections with Indigenous Old Ones, Elders, and knowledge keepers. Students participated in land- and water-based activities and ceremonies, learning circles, and writing workshops, and were invited to develop and share culturally grounded frameworks to inform their frontline practice with children, youth, families, and communities. Drawing on a storytelling approach to share our learning from this institute, we explore the praxis and challenges of resurgence in deeply damaging colonial contexts. Our individual and collective reflections on Indigenous land-based pedagogies focus on local knowledges, our own diverse perspectives and frontline work, and ethical land and community engagements as integral to resurgent Indigenous practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".