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Record W7132924565

Transcending the Classroom: Re-imagining Social Justice Education for K-12 Teachers in the Greater Toronto Area

2024· dissertation· W7132924565 on OpenAlexfundaboutno aff
Sara N. Pagliaro

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
FundersOffice of International Science and EngineeringSocial Sciences and Humanities Research Council of Canada
KeywordsSocial justiceInclusion (mineral)Economic JusticeSpace (punctuation)Teacher educationCritical theorySemi-structured interviewQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Social justice education (SJE) is increasingly emerging in importance in kindergarten to grade 12 (K-12) education in public schools in Toronto and the Greater Toronto Area (GTA). Considering this emergence, the study presented focuses on the stories, narratives, and personal experiences of K-12 public school teachers in Toronto/GTA who are also currently Social Justice Education (SJE) students at the Ontario Institute for Studies in Education. The findings from three semi-structured interviews with three K-12 teachers/SJE students reveal how they understand SJE and apply their SJE education in their pedagogical praxis. Additionally, the findings detail how these individuals navigate tensions related to SJE in relationships and interactions with their teacher-peers at school. The study concludes with a discussion on the significance of the findings and recommendations related to SJE implementation beyond the classroom. Ultimately, the study creates space for critical pedagogical considerations that promote SJE, diversity, and inclusion in 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 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)0.005
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 score0.499
Threshold uncertainty score0.997

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0340.036
Scholarly communication0.0130.008
Open science0.0020.015
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.480
Teacher spread0.376 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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