Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".