Indigenous Sign Languages of North America
Bibliographic record
Abstract
Indigenous groups across North America have vast cultural and linguistic diversity. Specifically, in a linguistic sense, there are a broad array of Indigenous languages that encompass both the auditory-vocal (spoken) and the visual-gestural (signed) modalities. Herein, the focus is drawn to a comprehensive literature review of Indigenous signed languages that have been historically used in North America, including Plains Indian Sign Language, Inuit Sign Language and a modern attempt at creating an Indigenous-based sign language Oneida Sign Language. Beginning with an overview of what sign languages are, the euro-western sign languages that exist in Canada as well as their key components. Subsequently, an overview of the history of the Indigenous sign languages - from first mythological accounts to formal documentation, where they have been used, how they came to be and how they have been used for the Deaf Indigenous community, the hearing Indigenous community, and as Lingua Franca’s to surmount linguistic barriers. Further, the various conservation attempts that have been made are discussed. As well as factors such as residential schooling, linguistic Darwinism and failures in academic documentation that have led to the decline of these languages in North America. Overall demonstrating the tragedy it has been to gradually lose these languages and culminating in a call to action to the individual as well as government organizations to ensure the preservation of these Indigenous sign languages for future generations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".