“It Makes My Heart Smile When I Hear Them Say, ‘Hi Grandpa, We’re Home!’”: Relationality, Alaska Native Wellbeing and Self Determination in Tribal Child Protection
Bibliographic record
Abstract
Before colonization, Indigenous child protection looked like an interdependent community. Indigenous knowledges and relational actions kept all within its fold safe and well. Colonial dispossession of land, degradation of subsistence rights, boarding schools, ongoing child removal, capitalism, and systems of oppression attempted to disconnect Indigenous peoples from their language, lands, ceremonial practices, stories, dances, songs, family, community, and themselves. However, Indigenous communities have held on, persevered, and have begun to turn the tide of intergenerational trauma through the revival of Indigenous wellness and self-determination. We believe local-based Indigenous relational knowledges can end colonial harm and promote wellbeing for all families and children. Our work builds off an Indigenous Connectedness Framework that recognizes the importance of the interrelated wellbeing of a person, family, community, ancestors/future generations, and the Earth. This framework was adapted based on community feedback to better fit the Nome Eskimo Community (NEC) and Bering Strait regional context. This paper shares results of community focus groups that led to the creation of a NEC Piaġiq (wellness) Framework, and shares intentions for pilot implementation of a wellness curriculum and pilot intervention. We will offer insights and lessons learned. We believe self-determined Indigenous wellbeing efforts can lead to improved outcomes for our sacred children and families for generations to come.
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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 | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".