Bibliographic record
Abstract
The work of resurgence has been a primary focus of my research for some time as an Anishinaabe health, wellbeing, and physical activity researcher. I have focused on telling many Anishinaabek stories about resurgence through physical activity. But I have yet to tell my own. In my 2020 book, Indigenous feminist gikendaasowin: Decolonization through physical activity, I argued that physical activity has the power to disrupt embodied settler colonialism, to regenerate deep physicality, empowerment, and foster gwekisidoon gibimaadiziwin, which is to make positive changes in your life for wellbeing. My autoethnography is a deeply personal example of how I am navigating this journey of healing childhood trauma, growth, and resurgence. This is a personal decolonization process that has been growing all my life. I imagine myself as a full-grown spruce tree at this stage in my life, but I am now paying attention to my broken and crooked branches, the tilt of my stature, the spaces in between my boughs. As I continue to grow into a mature tree, I want to be fully aware of my imperfections, to welcome and understand them, to accept me for me. Rather than attempt to ignore them, imagining the spaces as not there, erased as embodied settler colonial spaces. These spaces and crooked branches are me, after all. In this autoethnographic article, I will share my own dibaajimowan/personal story of how I come to this work; how I grapple with settler colonialism and how I choose to use martial arts to strengthen my ability to address intergenerational trauma. Thus, in this paper, I ask the following questions: how is martial arts, BJJ in particular, helping me to address my childhood trauma? And, connecting to a broader Indigenous social issue, how can this self-study inform other Indigenous peoples, in particular women, who are navigating healing from trauma, whether it is personal, childhood or intergenerational trauma? It is a well-known practice amongst Anishinaabek to take up our responsibility to share our teachings so that others may learn. In speaking from my heart about these topics, I can share my story to demonstrate how I choose to grapple with settler colonialism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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 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".