Plants as Our Relatives: the creation and gifting of a relational learning task
Bibliographic record
Abstract
Transformative land-based learning experiences, facilitated by Instructors and guided by Elders, Ceremonialists, Knowledge Holders, and community members, bring learners from diverse communities together to deepen understanding of Indigenous perspectives and experiences. In response to the Truth and Reconciliation Commission of Canada's Call to Action #63.C “building student capacity for intercultural understanding, empathy, and mutual respect”, a unique relational learning experience titled Plants as Our Relatives was designed to extend “intercultural understanding empathy, and respect” beyond human relationships to our more than human relatives; our relatives from the soil—the diverse plants that have nourished these lands—and all that live there within—since time immemorial. This relational experience was then shared with non-Indigenous science colleagues who accepted this gift and adapted it for a third-year Biology land-based learning course that compares Indigenous ways based on relationships with creation and respect for the natural order of life to Western ways based on maximizing land productivity and land management. This perspective paper describes the Indigenous worldview that led to the creation of “Plants as Our Relatives” followed by the implementation and impact of integrating this learning experience into the post-secondary Biology curriculum.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".