Sense of Belonging and Social Climate in an Official Language Minority Post-Secondary Setting
Bibliographic record
Abstract
Perceived sense of belonging and positive social climate on campus are crucial elements for post-secondary students, as they contribute to academic achievement, positive mental health, and help-seeking. Few studies have explored post-secondary students’ sense of belonging and perceptions of social climate in an official language minority campus, which attract Canadian-born francophones, anglophones who pursue higher education in their second language, and francophone international students. With declining student mental health and greater ethnolinguistic diversity of post-secondary students on Canadian campuses, this important study aims to explore francophone students’ perceived sense of belonging and social climate on campus. In total, 35 students from different ethnolinguistic backgrounds took part in focus groups or individual interviews. Domestic students with French as their first language more often reported positive social climate on campus and a sense of belonging, in contrast to international students and students with French as a second language. A common obstacle to connecting with others was language insecurity in one of the official languages, as both are currently used on campus. Universities hosting students of multiple linguistic diversities should provide courses and campus events to stimulate intercultural knowledge and dialogue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".