"And BAM. You Have a Connection”: Blind/Partially Blind Students and the Belonging in Academia Model
Bibliographic record
Abstract
Belonging has significant impacts on success in postsecondary. Blind people are underrepresented in postsecondary and lack equitable opportunities to develop a sense of belonging. To build a better understanding of this underrepresented experience, this study shares narratives of 28 Blind students from across Turtle Island (and what is colonially called Canada) using Teng et al.’s (2020) Belonging in Academia Model (BAM) as a conceptual framework. Thematically analyzed findings suggest that blind students’ perspectives offer nuance to the BAM’s conceptualization of how belonging develops. For blind students, external factors such as class size are especially important in determining affiliation with an institution. Blind students elucidated the importance of familiarity with different ways of being in the world, including blindness. Third, acceptance involved having their blind embraced in postsecondary contexts. Forth, interdependence was key to building trusting connections for blind students. Fifth, blind participants discussed equity at length related to access, the added work of trying to obtain access, and the emotional work involved. This study helps fill a gap regarding the experiences of these traditionally underrepresented postsecondary students in Canada. The BAM may raise the consciousness of stakeholders in considering the unique factors impacting belonging for blind people. By attending to these perspectives, stakeholders can become more responsive to the experiences of people from equity-deserving groups. Understanding facilitators and barriers to belonging could result in culturally safer practices and inclusive pedagogical practices and system policies. Only when we create a space where everyone can belong will higher education be truly inclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".