Taking Responsibility in Land-based Learning from a Racialized Woman's Perspective in Canada
Bibliographic record
Abstract
This chapter highlights my responsibilities in land-based learning for establishing meaningful relationships with Indigenous communities in Canada. Understanding and taking responsibility for land-based learning as a process of decolonization is crucial to holding people accountable. Decolonization in land-based learning is a lifelong journey to have tough conversations and educate ourselves about raising our awareness of Indigenous people, successful stories, and land. In this chapter, I discuss why responsibilities in land-based learning will provide belongingness to me as a racialized woman in this Indigenous land. Mother Earth has been providing mankind with everything we need, however, as a community and society we have never given anything back in return. Land-based learning is something I continue to learn from, and it helps with self-reflection. Land-based learning supports reconciliation by bringing life into Indigenous culture. Being a racialized woman, it is my understanding that there is so much importance and value when it comes to land-based learning as it is able to revitalize traditional Indigenous practices such as language, community, and education. These are key concepts to push the understanding of Indigenous people and learning to take responsibility. For several decades, so many voices have been diminished by the overpowering voices of the colonizers. It is important to understand that the land-based learning goal is to extinguish the colonizer’s lens and incorrect ideology of Indigenous people being inferior to their white counterparts. The objective of this chapter is to recognize and acknowledge the immediate issues of Indigenous people while discussing the importance of reconciliation through land-based learning. Many may struggle to understand the idea of reconciliation as many may think a simple apology is the correct path for healing and reconciliation. Although, it is a start, a simple apology does not suffice in the massive picture of Truth and Reconciliation. “To the Commission, ‘reconciliation’ is about establishing and maintaining a mutually respectful relationship between Aboriginal and non-Aboriginal peoples in this country” ( Fontaine, 2015 ). Reconciliation is much more than apologizing for the past, it is meant to highlight the dark past of Canada while creating a mutual acceptance of the inequality many Indigenous people had to face. That is why land-based learning is influential in presenting reconciliation as more than an apology. Reconciliation through land-based learning is multifaceted and requires time and dedication.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.101 | 0.025 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".