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Record W3096369624 · doi:10.1080/03057240.2020.1832451

Complexity in restorative justice education circles: Power and privilege in voicing perspectives about sexual health, identities, and relationships

2020· article· en· W3096369624 on OpenAlexafffund
Christina Parker, Kathy Bickmore

Bibliographic record

VenueJournal of Moral Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPedagogySociologyPower structureCurriculumCritical pedagogyPsychologyEthnography

Abstract

fetched live from OpenAlex

Restorative justice pedagogies, such as dialogue or peacemaking circles, allow students to learn how to share and listen with peers, set boundaries for moral dialogue, and engage constructively with each other’s perspectives. This study is part of a larger project focused on teachers’ professional development and circle implementation. The focus of this article is on one teacher’s approach to using circles in teaching her intermediate health curriculum unit, situated in a school with a strong restorative justice initiative. In this restorative classroom, dialogue was integrated into regularly enacted academic as well as interpersonal curriculum; this interrupted, or at times reaffirmed, the status quo. Data includes classroom observations, professional development observations, teacher and student interviews, and a reflective researcher journal. Dialogue enacted in this classroom illustrated moral issues students grappled with, relating to sexual health, inclusive sexual identities, and sociocultural relationships. Results illustrated how the teacher’s pedagogical choices transmitted values and shaped opportunities for critical dialogue, and that students’ social and cultural capital impacted how certain topics were discussed.

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)0.011
metaresearch head score (Gemma)0.021
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 score0.020
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0200.054
Scholarly communication0.0150.015
Open science0.0020.029
Research integrity0.0020.006
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 head0.209
GPT teacher head0.435
Teacher spread0.226 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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