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Record W4387812783 · doi:10.17356/ieejsp.v9i2.1117

Creative and participatory methods for bolstering violence prevention in schools in South East Europe through shifting social and gender norms

2023· article· en· W4387812783 on OpenAlexaff
Kathleen Manion, Laura H. V. Wright, Vanessa Currie, Laura Lee

Bibliographic record

VenueIntersections · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsInternational Institute for Child Rights and DevelopmentRoyal Roads University
Fundersnot available
KeywordsCitizen journalismParticipatory action researchGender studiesPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

Children have the right to be free from violence in schools, yet violence in schools persists. The social and gender norms, or unwritten rules of behavior that drive our collective beliefs, attitudes, and perspectives, perpetuate both positive and harmful behavior related to violence. However, social norms are malleable. To explore this further, the Regional‌ ‌Research‌ ‌on‌ ‌Violence‌ ‌Against‌ ‌Children‌ ‌in‌ ‌Schools‌ ‌in‌ ‌South‌ ‌East‌ ‌Europe project, supported by Terre des hommes and the Child Protection Hub and led by the International Institute on Child Rights and Development (IICRD) sought to work with young people and their supporters in eight South and East European countries from 2019–2021 to unpack how social and gender norms impact school related gender-based violence (SRGBV) and the potential role young people play in challenging destructive social norms in Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Kosovo, Moldova, Romania, and Serbia. At the intersection of a child’s right to be safe, to be educated and to be heard, this paper looks at a creative and participatory research approaches that bring children’s experiences of violence in schools to the fore and centers their experiences. This largely qualitative study drew on participatory and creative methods to explore the incidence and type of violence that children and young people are facing in and around school in South East Europe, who is most impacted by it, the social and gender norms related to violence (including SRGBV) against children, the mechanisms and child-led actions that protect children from violence and promote wellbeing, how children and young people felt able to prevent or respond to violence (and SRGBV specifically), and the ideas they had for prevention. National academic and independent researchers were trained on the research and analysis tools that were designed by the IICRD team to ensure consistency. Two schools in each country were chosen to run 2–3 day workshops with up to 15 young people aged 13–18 years old and up to 15 adult supporters in each site. This paper outlines the findings and focuses on the cyclical nature of research and practice where one informs the other. The multi-country research design and findings offer unique insights into effective approaches to work with young people as well as the levels of violence experienced by young people and their critical insights in how to implement enhanced safety in schools. In addition, this paper emphases the process of conducting research using creative and participatory methods as this is not often discussed in detail in the literature. In order to develop research with children and young people that can effectively impact practice, we suggest it is imperative to have a relational approach embedded in research that provides training for adults to ensure they are equipped to do this sensitive work.

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)0.060
metaresearch head score (Gemma)0.018
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 score0.060
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.013
Scholarly communication0.0060.004
Open science0.0020.012
Research integrity0.0010.002
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 head0.235
GPT teacher head0.480
Teacher spread0.245 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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