An intersectional analysis of students with disabilities’ exam experiences
Bibliographic record
Abstract
This study examined how Australian students with disabilities’ intersecting identity positions shaped and were shaped by their exam experiences. We conducted semi-structured interviews with twelve university students registered with disability services and sharing additional minoritised identities. These were analysed to explore structural, political, and representational intersectionality. Thinking with theory, we illustrated how students’ identities intersected to undermine offered accommodations’ effectiveness. Structurally, students with multiple minoritised identities struggled to prove exam impacts and gain equitable adjustments. Representational intersectionality highlighted how self-representations and/or concerns about others’ views of them undermined exam help-seeking. Political intersectionality analysis foregrounded how policy only recognised and supported some identities. Our analysis highlighted the complexity of students with disabilities’ identities; current single-axis, accommodations-based systems seldom created equitable exam experiences. Where possible, exam flexibility should be inbuilt, reducing the need for accommodations. Assessment policy must also allow staff agency to develop solutions with students that lead to greater equity.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".