Prevalence of Sexual Harassment of Female Students of Tertiary Education in Taraba State, North East Nigeria: Implications for Counselling
Bibliographic record
Abstract
The study was set out to investigate prevalence of sexual harassment of female students of tertiary education in Taraba State North East, Nigeria. One research question and one hypothesis were formulated to guide the study. The researcher used “Sexual Harassment of Female Students of Tertiary Education Questionnaire” (SHOFSOTEQ) to collect information for the investigation. The instrument was made up of 14 items and had a reliability coefficient of 0.85. It had content validity and language appropriateness. The researcher used three research assistants to administer copies of the questionnaire on the respondents. The researcher chose 2.50 as a benchmark for either agreeing or disagreeing with each of the items. The One-Way Analysis of variance (ANOVA) was used to test the hypothesis at 0.05 level of significance. The study found out that: there is prevalence of sexual harassment of female students of tertiary education in Taraba State. Sexual harassment of female students’ were carried out through inappropriate sexual comments, unwanted touching of female students’ breasts, tapping of female students’ buttocks and enticing of female students with high scores for sex amongst other. The result also revealed that there is no significant difference among the respondents in the universities, polytechnics and colleges of education on the prevalence of sexual harassment of female students. One of the recommendations is that authorities of tertiary educational institutions should put in place adequate measures to ensure that lecturers do not leak examination questions to students.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".