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Record W4406926793 · doi:10.53103/cjess.v5i1.291

Building Trust and Recreating Community in Online Classrooms through Cultural Responsivity

2025· article· en· W4406926793 on OpenAlexaffabout

Bibliographic record

VenueCanadian Journal of Educational and Social Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsResponsivityPsychologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates how five elementary school teachers established trust and recreated community for their online learners via cultural responsivity during the COVID-19 lockdowns in St. John’s, NL. The study adopts the Cultural Historical Activity Theory as a theoretical lens for this qualitative case study. The elementary school teachers in this sub-study are part of the Canadian section of the larger ADVOST project that promotes young children's inclusion and agency via culturally relevant arts and digital media. The research findings from analyzed interviews show that the elementary school teachers successfully fostered trust and emotional connections with their online students by creating inclusive, culturally responsive environments that foster collaboration, communication, and family involvement. Consequently, the researchers recommend adopting culturally responsive pedagogical practices and undertaking professional training on Equity, Diversity and Inclusion (EDI) with provisions for building trust and connection among online learners.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.016
Scholarly communication0.0080.005
Open science0.0020.015
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.112
GPT teacher head0.432
Teacher spread0.320 · 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 designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractyes

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