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Record W3186522795 · doi:10.1177/08295735211034713

The Relationship Between Social Support and Student Academic Involvement: The Mediating Role of School Belonging

2021· article· en· W3186522795 on OpenAlexafffundabout
Luis Francisco Vargas‐Madriz, Chiaki Konishi

Bibliographic record

VenueCanadian Journal of School Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsGraduation (instrument)PsychologyMediationSchool climateSocial supportAcademic achievementDevelopmental psychologyMultilevel modelSocial psychologyMathematics educationSociology

Abstract

fetched live from OpenAlex

Canada’s high school graduation rates are still low when compared to other members of the OECD. Previous studies have found academic involvement is associated with positive trajectories toward graduation, that social support promotes student engagement, and that school belonging could mediate this relationship. Still, little is known about the specificity of such mediation, especially in Québec. Therefore, this study examined the role of belonging as mediator of the relationship between social support and academic involvement. Participants ( N = 238) were high-school students from the Greater Montréal Area. All variables were measured by the School-Climate Questionnaire. Results from hierarchical multiple regressions indicated parental support had a direct relationship, whereas peer and teacher support had a mediated relationship by school belonging with academic involvement. Results highlight the critical role of school belonging in promoting academic involvement in relation to social support.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.593
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.125
GPT teacher head0.428
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations59
Published2021
Admission routes3
Has abstractyes

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