At the intersections of ableism and linguicism: Stories from neurodivergent undergraduate students who speak English as an additional language
Bibliographic record
Abstract
Higher education has often been framed as a necessary step for personal development, because a university education is seen as a gateway to a prosperous future. Yet, systemic ableism and exclusionary practices deeply affect the educational experiences and learning outcomes for students who are members of historically marginalized communities. For neurodivergent undergraduate students who speak English as an additional language (EAL), these barriers are compounded by institutional policies and practices that reinforce normative assumptions about ability and success. Teacher-centered approaches in higher education frequently exclude students whose non-normative ways of knowing, learning, and communicating differ from the “norm.” This study explores the undergraduate experiences of Adela and Imani (pseudonyms), two neurodivergent EAL students. Their perspectives are drawn from five semi-structured narrative interviews. Together, their narratives illustrate systemic inequalities present in higher education, while also highlighting how intersecting structures of ableism, linguicism, and racism shape their academic trajectories. By situating their educational experiences within the broader North American post-secondary landscape, this research highlights how instructional policies and practices perpetuate marginalization and exclusion. It calls on educators and institutions to dismantle the systemic barriers that disproportionately affect neurodivergent EAL students and to foster more equitable learning environments.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".