Circles and lines: indigenous ontologies and decolonising climate change education
Bibliographic record
Abstract
In 2015, The Truth and Reconciliation Report (TRC) was released in Canada, outlining 94 Calls to Action which, include pushing Canadian post-secondary institutions to ethically engage Indigenous communities and knowledge systems.11 Truth and Reconciliation Commission of Canada (TRC), Honouring the Truth, Reconciling for the Future: Summary of the Final Report of the Truth and Reconciliation Commission of Canada. Truth and Reconciliation Commission of Canada, 2015. https://ehprnh2mwo3.exactdn.com/wp-content/uploads/2021/01/Executive_Summary_English_Web.pdf. This paper seeks to respond to the TRC by offering a spatial analysis of the differences, broadly conceived, between Indigenous and western ontological structures. We consider these differences in terms of ‘circles and lines’ through a novice, settler understanding of how Mi’kmaw concepts of etuaptmumk (two-eyed seeing), netukulimk (conservation laws) and m’sɨt No’kmaq (all our relations) can be brought to support decolonial teaching and learning about such important and urgent matters as climate change. A related goal in this paper is pedagogic: we hope our own ambivalent learning here can be used as an example to reflect deeply on how settlers like us might/should/can’t work with the ethical, political, and practical challenges of responding to the TRC in our research, involving, and considering Indigenous ways of knowing and being.22 Scott Kouri, ‘Settler Education: Acknowledgement, Self-location, and Settler Ethics in Teaching and Learning’, International Journal of Child, Youth and Family Studies 11, no. 3 (2020): 56–79, https://doi.org/10.18357/ijcyfs113202019700.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.061 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".