Self-Location as a Disruptive Counternarrative in Teaching and Learning
Bibliographic record
Abstract
Colonization has had deep and lasting impacts on the Indigenous people of Canada and continues to be prevalent today as we unearth the human cruelty and impact inflicted through politics and religious institutions in partnership with the colonial agenda. With the Covid-19 pandemic impacting the global community, light is being shed on the political corruption and disregard for human life on a socio-economic scale that has plagued the colonial world for centuries. In Canada, systemic racism has attempted to silence Indigenous peoples through the Indian Act and the residential school system with the intention of assimilation or extinction by denying their culture, traditions, and language. Their strong sense of community and cultural practices have allowed them to survive this genocide that is ongoing as living conditions and human rights are not being significantly improved or acknowledged. This chapter explores self-location, an Indigenous research methodology, as a counternarrative to teaching and learning intended to assist in guiding the journey to decolonize our society through allyship. This approach is intended to develop a better sense of self and understanding of our own unique roles as members of a community. Self-location is explored as a counterpart to teaching and learning that supports decolonization and learning throughout the curriculum. Through educational reform, self-location is intended to develop the human spirit through the practice of empathy, forgiveness, love, and compassion, emphasizing the necessity of accountability and responsibility as citizens who belong to a global community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".