Links between school climate and bullying: A study of two tribes schools
Notice bibliographique
Résumé
Bullying is a problem for schools around the world, and is an important topic for research because it has been associated with negative outcomes on several social, psychological, and academic measures. Antibullying programs have varied greatly in their outcomes, with some studies reporting positive results while others have reported little or no positive impacts. This could be due, in part, to insufficient attention paid to school climate as a possible mediating variable. My dissertation aims to explore the links between school climate and bullying/victimization. Because Tribes (Gibbs, 2001) is a well-developed program intended to improve school climate and is becoming increasingly popular in schools, it was used to explore the links between school climate and bullying/victimization. Tribes is a program that uses a learning-community, whole-school model and aims to create a positive school climate through improved teaching and classroom management, positive interpersonal relations, and opportunities for student participation. A case study methodology was used and data was collected from 2 Tribes elementary schools. One school was in its first year of implementation, and the other school was in its fourth year of implementation. Data sources included: surveys of grade 4-6 students, teacher surveys, student focus groups, teacher semi-structured interviews, classroom and general school observations, teacher focus groups, and interviews with non-teaching staff members. Data from this study indicate which aspects of the school climate may be most important for creating a bully-free environment, and a model is proposed describing possible mechanisms through which school climate can be changed to produce an environment less conducive to bullying. The results of this study also provide local knowledge to the two schools involved regarding the perceived impacts of the Tribes program on school climate and bullying in their schools, and what can be done to further improve school climate and reduce bullying in their schools.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».