Tending our garden: Capacity building in the Myaamia community
Bibliographic record
Abstract
Language revitalization, as a movement or emerging field in the United States, is only approximately three decades in the making for most tribal communities. Its beginnings appear to coincide with the passage of the 1990 Native American Languages Act and the voices of alarm from linguists’ and native communities that attempt to raise awareness of the impact the loss of languages will have on communities. Three decades is just enough time to get a glimpse of the complex social, political and cultural issues that face tribal communities as they attempt to breathe new life into their languages. The Myaamia effort was born during this time of great uncertainty. Adding to this complexity was the reality that the Myaamia community did not have any remaining speakers by the mid 20th century, but extensive archival materials spread across the country and parts of Canada. With no models to follow for development of their unprecedented effort, a core of individuals set out to see what might be possible. This is a story of perseverance in the face of extensive obstacles. This personal account articulated by two individuals (Julie Olds and Daryl Baldwin), both citizens of the Miami Tribe of Oklahoma, and central to the launch of this effort, provide an in-depth account of one community’s attempt to claw their way into the present and set a course of accomplishments that would serve as the foundation for future growth. To aid in their understanding, Julie developed a metaphor of an unattended garden to help make sense of what they were up against. The garden metaphor drives a powerful intellectual space in this effort and is woven throughout this story as they recount their experiences and struggles to achieve something for the benefit of their community and tribal nation. A core value in Myaamia culture is the notion of ayaakwaamisioni ‘striving to achieve’ and this account is an example of that highly valued cultural attribute. Their journey, in their own words, is personal and reveling. They share it here in hopes that other communities will find parallels with their own language journeys and comfort in knowing that great accomplishments and benefits can in fact be realized from archive-based efforts in language and cultural revitalization.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.028 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".