Decolonizing disability: Teachings from Tx eemsim and voices from the lands of the Nisg a’a Nation
Bibliographic record
Abstract
Indigenous perspectives regarding disability are underrepresented in scholarly literature. This article profiles traditional perspectives and contemporary experiences regarding disability through voices from the lands of the Nisg ̱ a’a Nation. Influenced by Indigenous theory, this case study is based on semi-structured interviews with six diverse Indigenous community leaders including Simgigat (Hereditary Chiefs) and Sigidim Haanaḵ’ (Matriarchs). Four themes emerged: (1) Indigenous laws and cultural protocols enact principles of equity and inclusion; (2) language, kinship, and culture inform Indigenous perspectives regarding disability; (3) the gift of disability is celebrated through storytelling; and (4) colonization has negatively impacted disability for Indigenous Peoples. In addition, this study demonstrates that further research is needed on contemporary and historical disability policy in the Indian Act and disabilities in the context of Indian Residential Schools. This research considers the imposition of Western and colonially constructed disability identities while demonstrating that Indigenous knowledges, traditions, and practices are crucial to decolonizing understandings of disability in Canada and around 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".