Framing Deaf Children’s Right to Sign Language in the Canadian Charter of Rights and Freedoms
Bibliographic record
Abstract
Sign language rights for deaf children bring a unique perspective to bear in the fields of both disability rights and language planning. This is due to the lack of recognition in existing case law of the right to language in and of itself. Deaf children are frequently deprived of early exposure to a fully accessible language, and as a consequence may develop incomplete knowledge of any language. Thus, in the case of deaf children the concept of sign language rights encompasses rights that are ordinarily viewed as more fundamental to human equality. This paper will take as a starting point the historical treatment of the enumerated disability ground in the Canadian Charter of Rights and Freedoms’ section 15(1) guarantee of equality rights. We argue that in order to meet deaf children’s specific biological and linguistic needs, these children’s right to sign language also needs to be recognized as an analogous ground for protection from discrimination. Sign language rights are framed in terms of an immutable characteristic of all children, namely the biolingual process for language acquisition. The biolingual process is the experiential and innate ability to acquire language.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".