Healing historical trauma through resurgence and radical resistance
Bibliographic record
Abstract
For 500 years, Indigenous peoples of Turtle Island have had to contend with colonial tactics intended to displace, dispossess, disconnect, and disempower Indigenous peoples from their relationships with their territories and their cultural identities. Historical Trauma theory explains these colonial experiences cause disruption and disconnection within Indigenous peoples who now exhibit historical trauma symptoms. This research project is essential because it focuses on how Indigenous peoples can heal from these experiences. Grounded in an Indigenous research paradigm, six participants engaged in six individual one-on-one visits and four days of group work, revealing how resurgence and radical resistance heals historical trauma. Findings show that healing does not occur in isolation, nor is it based solely on the present. For the participants, healing historical trauma by choice is key, the past, present and future is understood through radical resistance, and resurgence resists the compounding affects of historical trauma. Recommendations speak to the necessity of the individual beginning their healing journey, radiating outwards to include family and community to be supportive and to join in on the healing. There is a need for advanced healing programming, education reform, helpful allies, and an anti-colonial shift in how the state provides funding to Indigenous organizations and communities who are engaged in healing practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".