The Impact of Language Revitalization Efforts on Indigenous Cultural Practices: A Case Study of the Tahltan, Cherokee, and Lakota Nations
Bibliographic record
Abstract
Historically (and currently) Indigenous languages have been suppressed and marginalized within society, inspiring declining levels of language usage and L1 and L2 speakers. It can be argued that these declining levels of speakership have impacted the tangible and intangible elements of Indigenous cultural practices. Although Indigenous peoples have faced punishment for using their languages, the reclamation or revitalization of Indigenous languages can lead to the recovery of cultural knowledge and, in the process, help heal the trauma caused by colonization. This article seeks to address the impact language revitalization efforts can have on maintaining the cultural practices of Indigenous communities by examining three case studies of ongoing revitalization efforts: the Tahltan Nation, the Cherokee Nation, and the Lakota Nation. Moreover, a theoretical analysis will be conducted following the Sapir-Whorf Hypothesis of linguistic relativity and linguistic determinism. A review of these practical and theoretical examples demonstrates that the language we speak can shape our thinking patterns as well as how we are predisposed to view the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".