Teaching Activist Thinking in Canadian Education: The limitations of play-based learning and radical potential of Indigenous land-based learning
Bibliographic record
Abstract
Amidst a climate crisis induced by settler colonialism and capitalism, education is key to developing new tools and envisioning solutions. Fostering skills for children to critically engage with systems of power is fundamental to how the next generation will address urgent global issues. Drawing on decolonial methodologies outlined by Linda Tuhiwai Smith (2012) and Zoe Todd (2016), I question whether play-based models are successful in teaching activist thinking in Canada. As an educational framework, play-based learning is gaining interest, particularly following the COVID-19 pandemic that required children to adapt to outdoor learning. Forest Schools Canada is one example that claims to revolutionize how children interact with the natural world to develop eco-stewardship skills in an age of ecological collapse. However, I find that mainstream play-based methods are not necessarily radical when examined alongside Indigenous land-based learning. Both frameworks prioritize intergenerational relationship-building, immersive storytelling, and hands-on learning outdoors, but the intention behind Indigenous land-based learning is inherently decolonial and anti-capitalist by necessity; conversely, play-based learning can inadvertently perpetuate these damaging systems. Using auto-ethnographic professional experience, governmental policies, and Sandra Harding’s (2016) work on standpoint theory, I critique current examples of Canadian play-based education concerning their effectiveness in teaching activist thinking. I rely on Indigenous scholars in New Zealand and Turtle Island to inform academic theories of land-based learning with examples, supported by interviews with former Indigenous colleagues in eastern Canada. My narrative-like writing and inclusion of practice-based methodology—two video conversations—deviates from traditional qualitative research to foreground relationships consistent with the frameworks I discuss. Though play-based learning shows limited promise in deconstructing harmful structures of power, especially within established western contexts like public schools, storytelling has potential to generate meaningful change if layered with intention, such as naming root causes, linking to current affairs, and inviting creative solutions through play.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.036 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".