Teaching with Oppressive Language: A Call to Implement the Human Rights Model in Inclusive Education with Autistic Students
Bibliographic record
Abstract
The language and practices we use with autistic students perpetuates oppression, negatively impacts their well-being, and contributes to negative experiences at school and in society. Current practices in education systems are not complying with best practices as identified by autistic advocates and researchers. Through the focus on how models of disability shape the language people use when discussing the needs of students who are autistic, this paper examines the impact of the human rights model on language and practices in education as they relate to teaching autistic students. Changing the language used from person first and euphemisms to identity first and acceptance will promote authentic connection, well-being, and lay foundations for lifelong skills for autistic students.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".