Hear Our Languages, Hear Our Voices: Storywork as Theory and Praxis in Indigenous-Language Reclamation
Bibliographic record
Abstract
Storywork provides an epistemic, pedagogical, and methodological lens through which to examine Indigenous language reclamation in practice. We theorize the meaning of language reclamation in diverse Indigenous communities based on firsthand narratives of Chickasaw, Mojave, Miami, Hopi, Mohawk, Navajo, and Native Hawaiian language reclamation. Language reclamation is not about preserving the abstract entity “language,” but is rather about voice, which encapsulates personal and communal agency and the expression of Indigenous identities, belonging, and responsibility to self and community. Storywork – firsthand narratives through which language reclamation is simultaneously described and practiced – shows that language reclamation simultaneously refuses the dispossession of Indigenous ways of knowing and refuses past, present, and future generations in projects of cultural continuance. Centering Indigenous experiences sheds light on Indigenous community concerns and offers larger lessons on the role of language in well-being, sustainable diversity, and social justice.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".