Bibliographic record
Abstract
There is much research and growing attention on Gender-Based Violence (GBV), including rape against men and boys. There is substantial evidence indicating that sexual violence against men is common in most armed conflicts. Most of the research tends to focus on the prevalence and long and short-term impacts of sexual violence perpetrated against men during armed conflicts in different countries such as the former Yugoslavia, Democratic Republic of Congo, Syria, and Darfur. There is a need for a more in-depth understanding of male victims’ experiences in a holistic sense. Concepts such as resilience, courage, and connectivity are mainly overlooked, even though there are vehicles for growth and development. However, there are fundamental obstacles that male victims of SGBV are still facing, such as the lack of proper terminology to describe their experience. The current article sheds light on male victims conflicting feelings and struggles to conceptualize their sexual victimization experience as rape. Specifically, the article will explore the impact of lack of disclosure, minimization, and lack of proper terminology on the processing, healing, and recovery journey. The participants involved were male victims who endured sexual violence during the conflict in the Eastern region of Congo. Semi-structured interviews were carried out to explore the core aspects of their experiences of sexual victimization in relation to the esteemed concepts of masculinity. Consistent with previous research, findings show that male victims understand the word rape. Nevertheless, they find it challenging to employ the term rape to characterize their experiences of sexual victimization, as the term is predominantly linked to acts of sexual violence perpetrated against women and girls. Furthermore, victimhood is primarily incompatible with the dominant masculine script.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".