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Record W4200372404 · doi:10.47678/cjhe.v51i4.189087

Sense of Belonging and Social Climate in an Official Language Minority Post-Secondary Setting

2021· article· en· W4200372404 on OpenAlexaffvenueabout
Kailey Penner, Danielle de Moissac, Rhéa Rocque, Florette Giasson, Kevin Prada, Paul Brochu

Bibliographic record

VenueCanadian Journal of Higher Education · 2021
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité de Saint-Boniface
Fundersnot available
KeywordsFrenchDiversity (politics)Sense of communityPedagogyPsychologySociologyHigher educationSocial psychologyPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.362
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes3
Has abstractyes

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