Hazing in the military: A scoping review
Bibliographic record
Abstract
Introduction: Initiation rituals have long been part of military organizations, serving to create a sense of cohesion and commitment to the armed forces. However, hazing, which involves harassment, humiliation, and abuse, can cause severe mental and physical harm to military personnel and erode operational morale and effectiveness. Although hazing goes against core institutional values of ethical behaviour and professional integrity, it continues to occur and has received limited empirical attention in military organizations. Methods: This scoping review aimed to provide an overview of what is known about hazing in the military by mapping the academic literature. The authors conducted a comprehensive search of PubMed, Scopus, Web of Science, EBSCO, and ProQuest for English-language, peer-reviewed research articles addressing hazing and initiation rituals in the armed forces. Results: On the basis of this scoping review, the authors argue new approaches are necessary on an organizational level, in practice and in policy, to address and prevent hazing in the military. More empirical research is also required on hazing, the connection between hazing and sexualized violence, and effective preventive mechanisms.
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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".