How social belonging and performance varies across demographic groups in first year students in science courses.
Bibliographic record
Abstract
Social belonging in science is important because it shapes students' academic experiences and influences their decisions to persist in their studies. Social belonging refers to the sense of acceptance, inclusion, and connection that individuals feel within a group. This study, reflects how students perceive their value and role as essential members of their class. Societal stereotypes can negatively affect students, sometimes leading to disengagement or withdrawal from university. Recognizing students' perspectives and creating inclusive learning environments can enhance belonging, participation, and academic success. My research investigates how course-level social belonging correlates with demographic factors such as gender identity, racialization, international/domestic status, and academic outcomes. This poster highlights the role of social belonging in shaping first-year science students' experiences and performance. We combined quantitative surveys and qualitative interviews with first year students, with data from 5074 students across six Canadian institutions. By assigning social belonging scores to survey responses, I identified patterns and factors linked to belonging and academic performance. Qualitative interviews provided deeper insights into students' experiences, revealing themes of isolation, underrepresentation, and challenges in connecting with peers. Students also recounted how inclusive teaching practices positively impacted their sense of belonging. Preliminary findings suggest that inclusive environments make higher engagement and confidence. This research has been approved by ethnics boards at all sites.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".