An autoethnographic exploration of disability discourses: transforming science education and research for students with learning disabilities
Bibliographic record
Abstract
In this autoethnographic inquiry, I examine the dominant disability discourses that inform practice and research in science education for individuals with disabilities. Guided by my experience as a practitioner-researcher, I use reflexive vignettes and photo elicitation to discuss and critique disability discourses (e.g., the medical and social models of disability) that construct students with learning disabilities (LD) as disadvantaged learners. For example, the medical model of disability pathologises students with LD by focusing on their individual deficits and blaming them for their academic struggles and failures in science. In contrast, the social model of disability locates the problem solely within the students' environment (e.g., teaching strategies) and does not consider within-individual issues (e.g., cognitive deficits). By navigating through these discourses, I found my voice as a practitioner-researcher in Bronfenbrenner's (2005) ecological model, which recognises that individuals' barriers stem from their characteristics as well as their complex, multilayered environment. This article, embedded within a reflexive process, illuminates my journey of self-transformation as a practitioner-researcher while transforming and bringing educational changes to the academic lives of my students with LD.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| 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".