The experiences of disabled and neurodiverse Ph.D. students in LIS programs during the COVID-19 pandemic: Weathering the storm1
Bibliographic record
Abstract
Amid the growing body of research on disability and neurodiversity, disabled and neurodiverse Ph.D. students are not often in the focus, despite the fact that Ph.D. students occupy a unique position of a learner-scholar-teacher in academia. A particular gap is felt in the field of Library & Information Science (LIS). This study stands to address this gap by focusing on the experiences of disabled and neurodiverse Ph.D. students in American and Canadian LIS Programs during the COVID-19 pandemic and in its immediate aftermath. Guided by the Holistic Empowering Methodological Approach (HEMA) that puts participants in the driver’s seat and allows them to determine the nature and extent of participation, the study spotlights participants’ experiences during the remote learning and returning to campus phases of the lingering public health crisis. The findings show that while there was a fair balance of positive and negative experiences during the earlier stage of the pandemic, the stage of returning to campus was associated with additional challenges and an overwhelming number of negative experiences. The article addresses personal, program-related, and environmental factors in both positive and negative experiences, using the findings as a basis for conclusions and recommendations to Ph.D. program administrators and faculty.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".