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Record W4293212397 · doi:10.33682/a55e-b08e

Peacebuilding Education to Address Gender-Based Aggression: Youths' Experiences in Mexico, Bangladesh, and Canada

2022· article· en· W4293212397 on OpenAlexaffabout
Kathy Bickmore, Najme Kishani Farahani

Bibliographic record

VenueJournal on Education in Emergencies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAggressionHarmCurriculumFocus groupIdeologyPeacebuildingStructural violenceSociologyGender studiesPolitical sciencePsychologyPedagogySocial psychologyPoliticsPublic administration

Abstract

fetched live from OpenAlex

Building durable peace through education requires addressing the gender ideologies and hierarchies that encourage both direct physical aggression and indirect harm through marginalization and exploitation. Although formal education systems are shaped by gendered patterns of social conflict, enmity, and inequity, schools can help young people to build on their inclination, relationships, and capability to participate in building sustainable, gender-just peace. In this paper, we draw from focus group research conducted with youth and teachers in public schools in Mexico, Bangladesh, and Canada to investigate how young people understood the social conflicts and violence surrounding them and what citizens could do about these issues; and how their teachers used the school curricula to address them. The research revealed that gender-based violence was pervasive in students' lives in all three settings, yet the curriculum the teachers and students described, with minor differences between contexts, included few opportunities to examine or resist the gender norms, institutions, and hierarchies that are the roots of exploitation and violence.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.340
Teacher spread0.304 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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