Creating an Inclusive Learning Community to Better Serve Minority Students
Bibliographic record
Abstract
As campuses become increasingly diverse, it is important that faculties maintain inclusive classrooms. Students of underrepresented ethnic/racial groups are more likely to experience disengagement in an academic setting (Nagasawa & Wong, 1999), which can lead to underperformance (Major et al., 1998). Students with LGBTQA+ (lesbian, gay, bisexual, transgender, queer, or asexual) identities are at higher risk of poor mental health and lower academic performance compared to cisgender and heterosexual students (Aragon et al., 2014). These detrimental experiences can lead to even more harm in a remote learning environment, where students have fewer opportunities to feel a sense of belonging and connect with their peers and/or instructors. This paper will consider strategies of inclusiveness in the online classroom and in-person learning environment within a social psychology framework to better support underprivileged students to improve academic performance and the overall educational experience. The suggestions and discussions provided apply to both in-person learning as well as remote delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".