MétaCan
Menu
Back to cohort
Record W2116550207 · doi:10.20355/c5qg6x

Peace Education in Canada: Teacher Perceptions of the Cultivating Peace Education Program

2010· article· en· W2116550207 on OpenAlexvenueaboutno aff
Afyare Abdi Elmi

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
Fundersnot available
KeywordsPeace educationCurriculumPedagogyPerceptionSociologyFlexibility (engineering)Conflict resolutionPolitical sciencePsychologySocial scienceManagement

Abstract

fetched live from OpenAlex

Many Muslims, Arabs, and other minority communities in Canada experienced the backlash of the September 11, 2001 events. Although these groups were discriminated against in a number of institutions, Muslim children in secondary schools in particular experienced different types of discrimination and violence. In order to help reduce incidents of discrimination in schools, with the help of academics at the Ontario Institute for Studies of Education (OISE), the Classroom Connections (non-for profit organization) developed a peace education program, the Cultivating Peace. Using qualitative methods, this paper examines the perceptions of teachers who used the Cultivating Peace program. Four themes emerged from the data collected for this research: flexibility, utility, relevance, and challenges. The findings reveal that educators believe the Cultivating Peace program promotes a culture of peace in Canada. Teachers find the Cultivating Peace flexible in that it fits well in the curriculum. In particular, teachers believe the program fits well in a social science and humanities curriculum. In addition, educators perceive the program is useful in teaching conflict resolution, communication, and problem-solving skills. They also find that the Cultivating Peace program is relevant to students’ lives because it teaches values that promote a culture of peace. Teachers mentioned two major challenges: lack of time and distribution problems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.362
Teacher spread0.345 · 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 designObservational
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

Citations4
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Contemporary Issues in EducationSame topicPeace and Human Rights EducationFrench-language works237,207