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Exploring Measures to Prevent School Violence in Korea through Overseas Cases : Focusing on Norway, Canada, and Finland

2023· article· en· W4324373010 on OpenAlexaboutno aff
Ha-Young Kim, Seung-Bo Sim, Seok-Hwan Choi

Bibliographic record

VenueKorean Journal of Sports Science · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPsychosocial Factors Impacting Youth
Canadian institutionsnot available
Fundersnot available
KeywordsSchool violencePolitical sciencePerceptionEconomic growthPsychologySocial psychologyEconomics

Abstract

fetched live from OpenAlex

The purpose of this study was to explore ways to prevent school violence through oversea cases. This study examined and compared school violence preventive programs in Norway, Canada, and Finland to provide good insight to use in Korea. Data analysis was performed by dividing into three parts: a)legal aspects, b)representative prevention programs, and c)application plans to Korea. The results are as follows. First, school violence is considered as a national problem, and school violence in overseas has been reduced due to active preventive activities. Second, overseas countries are using long-term prevention programs that have shown the effect of reducing school violence. Third, Korea should try to change its perception of school violence in the long run. Then, it becomes easier to use overseas school violence prevention programs.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.091
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.118
GPT teacher head0.337
Teacher spread0.219 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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