Perspective of inclusion in the academy navigating the environment: autoethnographic reflections of a disabled graduate student
Bibliographic record
Abstract
This study contributes to a growing depth of literature utilizing self-reflection and disability in higher education. It discusses the dynamics of concepts including academic ableism, racism, socio-cultural barriers, and environmental factors impacting the experiences of disabled students. The main objective of the study examines the level of social inclusion in the environment of the University of Manitoba. The methodological process uses analytic auto-ethnography for exploring the experience of a disabled graduate student in the academy. Findings of the study highlight several barriers which are obstructing the development of an inclusive environment for disabled students. These include the apparent lack of thoughtful physical planning for the inclusion of disabled students, presence of discriminatory socio-cultural assumptions, and over reliance on an accessibility team lacking in representation for inclusive procedures. Because centers like universities are made up of people and part of the larger society, the study proposes the model of inclusive consciousness for everyone as a way of promoting inclusion at all levels. Consequently, the study recommends that the way forward for the inclusion of disabled students is to create a framework that promotes equity, diversity and inclusion for disabled people, promotes the participation of disabled people in all aspect of the academy, ensures inclusion of disabled people in all forms of communication and makes accessibility issues a matter of general interest in the academy among others.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".