Bibliographic record
Abstract
In the foreword to this book, Joy Dolmage, founding editor of the Canadian Journal of Disability Studies, states (p. xxii): "I think we can all agree that, before the pandemic, our schools had too many unnecessary barriers in place for both students and faculty. COVID-19 has provided new reference points for evaluating long-standing social problems, allowing us to view old problems from fresh perspectives (Sherwood et al., 2021). If we want to push toward online education as just another temporary retrofit, then we need to build something that is much more accessible and sustainable." And so, the scene is set for what this volume advocates for. Co-editor Lisanne Binhammer states explicitly that the purpose of this book is not simply to critique, but to move to a place of active resistance. This volume, therefore, acts as a call to arms for what its contributors all claim needs to be exposed – that online pedagogy is somehow the solution to academic ableism. Her fellow co-editors, Chelsea Temple Jones and Fady Shanouda, state that the crux of this book is that while digital learning is touted as readily available to all, in-person and online approaches to such delivery remain inadequate. Instead of falling prey to simple yet persuasive arguments that online pedagogy is an automatic win for access, the intent of Online: Ableism and Access in Higher Education is to provide a new orientation to critical, digital, and accessible pedagogy.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.487 | 0.367 |
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".