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Record W3171336867 · doi:10.1080/17400201.2021.1937085

Teachers’ understanding and implementation of peace education in Colombia: the case of <i>Cátedra de la Paz</i>

2021· article· en· W3171336867 on OpenAlexaff
Esteban Morales, Engida Gebre

Bibliographic record

VenueJournal of Peace Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsChristian ministryPeace educationContext (archaeology)PedagogyCitizenship educationFace (sociological concept)Qualitative researchProfessional developmentCitizenshipPsychologyPolitical scienceMathematics educationSociologySocial sciencePoliticsGeography

Abstract

fetched live from OpenAlex

The longest internal conflict in Colombia was concluded in 2016, leading to the creation of Cátedra de la Paz, a peace and citizenship education course required for all schools and grade levels. However, teachers’ understanding of the course, their ways of implementation and the challenges they face remain unknown. This study examines teachers’ understanding of Cátedra de la Paz and the challenges they face when implementing it. Data were collected from 45 teachers using qualitative survey, followed by semi-structured interviews with 10 selected participants. Results showed that a) teachers have more integrated views of the course compared to the guideline provided by the Ministry of National Education, and b) teachers face several challenges implementing the course. Findings provide insights about context-oriented learning design for peace education and highlight the need for professional development and relevant support for teachers in a way that considers their understanding and implementation of peace education.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.147
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.405
Teacher spread0.379 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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