“I don’t like to be told that I view a student with a deficit mindset”: Why it Matters that Disability Studies in Education Continues to Grow
Bibliographic record
Abstract
In this article I use personal narrative to provide a commentary on the value of Disability Studies in Education (DSE). Through a mixture of recollections, observations, and descriptions, along with engagement with scholarship in the fields of both special education and DSE, I highlight ways in which I and other scholars have utilized the latter in our daily professional practices. First, I describe the point in my educational career when I came into contact with Disability Studies (DS). Second, I share the beginnings of how DSE came into existence through the work of a coalition of critical special educators. Third, I provide instances of DSE in action, highlighting a recent in-service presentation and other examples. Fourth, I explain why DSE is needed to protect and develop conceptualizations of disability outside of the traditional special education realm. Fifth, I illustrate the benefits of DSE’s interdisciplinary nature. Finally, I assert that DSE provides a visionary lens for improving educational practices for students with disabilities. In closing, I advocate for DSE’s continued growth in helping change deficit-based understandings of disability that continue to pervade education and society.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.051 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".