Nlaka’pamux justice systems: An investigation of Xitl’ix and the Lytton restorative justice prevention and education program
Bibliographic record
Abstract
This thesis investigates an Indigenous community’s traditional justice model called Xitl’ix, as well as the Lytton Restorative Justice Prevention and Education Program that is currently being utilized by members of the Lytton Indian Band. This qualitative study uses Indigenous Storywork Methodology and Narrative Inquiry, to explore the participants’ experiences with Xitl’ix and the Lytton Restorative Justice program. Due to the global pandemic, several video conference interviews, which the researcher refers to as virtual sharing circles, were held with eight participants. Six participants are from the Lytton Indian Band, one participant is a non-Status Indian and former director of the Lytton Restorative Justice Prevention and Education Program, and the last participant is a settler person who is a former high school principal. Each participant provided their perspective about Xitl’ix (Nlaka’pamux Court), the Lytton Restorative Justice Prevention and Education Program and/or Restorative Justice programs and services. The themes that emerged from the virtual sharing circles include Nlaka’pamux Knowledge, Indigenous healing, and ultimately provided a stronger foundation of understanding of the Xitl’ix teachings and the Lytton Restorative Justice Prevention and Education Program. During one of the virtual sharing circles and unexpected finding was revealed that described another Nlaka’pamux justice system, which has not been memorialized or described in past research about the Nlaka’pamux people.,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".