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Record W4416428703 · doi:10.1108/sojo-08-2025-0001

Social justice in public high schools: a literature analysis and field insights from two Canadian high schools

2025· article· en· W4416428703 on OpenAlexaffabout
Manu Sharma

Bibliographic record

VenueThe SoJo Journal Educational Foundations and Social Justice Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsSocial justiceContext (archaeology)CurriculumPsychological interventionEconomic JusticeWork (physics)Field (mathematics)Rural area

Abstract

fetched live from OpenAlex

Purpose This pilot study explores social justice education in the context of two schools in the interior of British Columbia that self-identify as advocates and promoters of social justice. Design/methodology/approach First, I identify key trends in recent (2014–24) literature on social justice education in high schools, noting the prevalence of three key themes: social justice programming, curriculum activities and teacher and leadership development. Then, drawing on the work of Iris Marion Young (1990) and Lee Ann Bell (2016), I analyze findings from my pilot study of four administrators, six teachers and four staff members from my two selected school sites. Findings While these early findings show some overlap with the findings of existing research, importantly, they also gesture at potentially substantial gaps in our critical conception of how social justice education is understood and practices, notably in rural Canadian schools. Originality/value Without studying diverse contexts within the landscape of Canadian high schools, this paper suggests, education researchers may be inadequately preparing teacher education programs and their teacher candidates for work in rural schools and may be missing the chance to broaden and diversify interventions in urban schools.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.340
Teacher spread0.325 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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