Prevalence and Demographic Differences in Harassment Tendency among University Students
Bibliographic record
Abstract
The current research was conducted to measure the prevalence and demographic differences in harassment tendency among university students. Cross-sectional research design was used in this study to measure the prevalence. The non-probability convenient sampling technique was used to select the sample size. The age of the students ranges between 17-26 years. The 620 students (310 males & 310 females) were assessed on harassment tendency through Harassment Tendency Scale (Mobeen & Bano, 2022). Equal number of male students (50%) and female students participated in the study (50%), the majority (65.3%) reported moderate harassment tendency. Only a small percentage (9.7%) fell into the high tendency category, while a quarter of the students 25% had a low tendency. Independent t-test results shows that female reported significantly higher harassment tendency score (M=59.95) as compared to male students (M=42.65). Rural students (M=53.58) have a higher harassment tendency score as compared to urban students (39.21). Harassment-related experiences, particularly encounters tied to identity, emotional, mental and physical health outcome.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".