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Record W2292726280 · doi:10.1080/00933104.2015.1100150

Pedagogical Tools for Peacebuilding Education: Engaging and Empathizing With Diverse Perspectives in Multicultural Elementary Classrooms

2016· article· en· W2292726280 on OpenAlexaff
Christina Parker

Bibliographic record

VenueTheory & Research in Social Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsPeacebuildingInclusion (mineral)PedagogyCurriculumViewpointsSociologyEmpathyMulticultural educationSemi-structured interviewQualitative researchMulticulturalismMathematics educationPsychologySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

This qualitative study used classroom observations, teacher and student interviews, and document analysis to examine the degree to which peacebuilding dialogue processes were implemented in 3 elementary school classrooms and how diverse students, particularly newcomer immigrants, experienced these pedagogies. The study critically examines how ethnocultural minority immigrant students aged 9 to 13 responded to conflictual issues pedagogies and discussions and how such pedagogies facilitated inclusive spaces for all students to participate in the curricula. The study’s results show that when content was explicitly linked to students’ identities and experiences, opportunities for democratic peacebuilding inclusion increased. In this research, diverse students’ classroom participation was influenced by various peacebuilding pedagogies, particularly when teachers engaged with conflictual issues in ways that encouraged students to develop acceptance and empathy for other perspectives. Using peacebuilding dialogue pedagogies to guide curriculum engagement with alternative viewpoints may contribute to diverse students’ inclusion in the classroom.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.406
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.475
GPT teacher head0.556
Teacher spread0.081 · 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.

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

Citations33
Published2016
Admission routes1
Has abstractyes

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