Academic Integrity, Ableist Assessment Design, and Pedagogies of Disclosure
Bibliographic record
Abstract
Having a more holistic understanding of accessibility in relation to academic integrity that goes beyond a discussion of learning disabilities and accommodation forms is necessary for higher education to be inclusive of disabled learners and critically explore the purpose of academic integrity equitably. This chapter first defines ableism and how it can manifest itself on campus and in online courses to then briefly frame the ableist nature of remote proctoring software and how assessment design is often itself ableist in necessitating proctoring software. The chapter will expand on the problematic nature of competition in high-stakes assessments that is necessarily at odds with accessibility and builds pedagogical barriers to reinforce how signature pedagogies in some disciplines can continue to support inequitable and ableist assessments. The chapter highlights the need to review assessment design and pedagogy to be accessible. It ends by emphasizing how trust needs to be built in educational spaces and that it is a lack of trust and opaque procedures that guides many of the inequitable and ableist academic integrity practices and policies in higher education institutions. It suggests four strategies to support assessment design that keep accessibility in mind and that in turn support a necessary conversation that centers citational ethics instead of surveillance that can harm disabled learners.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".