Addressing Domestic Violence through Circle Peacemaking in Alaska: Reflections on Building Tribal-Researcher Capacity
Bibliographic record
Abstract
This article first published advance online February 9, 2024We begin by acknowledging the impact of historical trauma on the community, as this formed the backdrop for the entire capacity building project. In January 2021, the Organized Village of Kake (OVK), Alaska, received funding for a planning grant from the National Institute of Justice through the Tribal-Researcher Capacity-Building Grant program. The project focused on how to incorporate domestic violence (intimate partner violence) cases into the Circle Peacemaking process, and on developing a proposal to study that process. The partnership team consisted of members of the OVK Tribal staff and independent researchers. The grant was awarded in the midst of the COVID-19 global pandemic, so all work on this project had to be conducted remotely. Of particular importance, Zoom allowed for face-to-face meetings, even though they could not be held in person. The partnership determined that a research study on use of Circle Peacemaking to handle domestic violence cases should centre an Indigenous research paradigm. The conceptual framework for the Circle Peacemaking process, rooted in Lingìt culture and life, is described. Existing strengths in the community that support the potential for using Circle Peacemaking in Kake to address domestic violence, potential measures of success, potential problems in carrying out a future study, and key learnings are also described.
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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 | 0.036 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| 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".