Flattening the Hierarchy: A Solution to the Male Problem of Bullying
Bibliographic record
Abstract
Bullying is a social problem that not only physically and psychologically affects victims but also disrupts the process of teaching and learning in senior high schools (SHS). We used an exploratory sequential mixed-method design comprising a survey and a double-blinded experiment to study the role of dominance hierarchy as the primary mechanism of bullying to effectively design anti-bullying strategies. The sample size used in the survey to validate the influence of dominance hierarchical structures on bullying was 79 students, while the experimental design to causally link dominance hierarchy to bullying included a sample of 21 students. The dominance hierarchy is one in which bullying behavior is exhibited as a means to rise up a social hierarchy. The current study validated that the dominance hierarchy is the more prevalent form of bullying in Ghana. We also observed that while bullying behavior sharply increases in SHS 3, bullying victimization does the opposite. This allows us to implement anti-bullying strategies for the most affected groups. Finally, the highest percentage of bullied individuals in SHS 1 are those who have a high social status relative to their peers, and the students who bully most frequently in SHS 3 are those who have a lower social status in school compared to their peers. Flattening the hierarchy is an effective way to significantly decrease bullying behavior. Therefore, measures such as increasing senior-junior cooperation through leadership positions, which are largely absent in SHS, will be effective at substantially decreasing bullying behavior in our 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".