ȻENTOL TŦE TEṈEW (TOGETHER WITH THE LAND)
Bibliographic record
Abstract
This article presents reflections from an Indigenous land- and water-based institute held from 2019 to 2020 for Indigenous graduate students. The institute was coordinated by faculty in the School of Child and Youth Care at the University of Victoria and facilitated by knowledge keepers in local W̱SÁNEĆ and T’Sou-ke nation territories. The year-long institute provided land-based learning, sharing circles, online communication, and editorial mentoring in response to a lack of Indigenous pedagogies and the underrepresentation of Indigenous graduate students in frontline postsecondary programs. While Indigenous faculty and students continue to face significant, institutionally entrenched barriers to postsecondary education, we also face growing demands for Indigenous-focused learning, research, and practice. In this article, Part 1 of a two-paper series on Indigenous land- and water-based learning and practice, we draw on a storytelling approach to share our individual and collective reflections on the benefits and limitations of Indigenous land- and water-based pedagogies. Our stories and analysis amplify our integration of Indigenous ways of being and learning, with a focus on local knowledges and more ethical land and community engagements as integral to Indigenous postsecondary education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".