Causes, Consequences and Strategies for Curbing Sexual Harassment in Tertiary Institutions in Kwara State
Bibliographic record
Abstract
This paper examines the causes, consequences and strategies for curbing sexual harassment in tertiary institutions in Kwara State. The study consisted of a sample size of 630 female students from three selected tertiary institutions in Ilorin metropolis. The instrument used was a questionnaire titled “Causes, Consequences and Strategies for Curbing Sexual Harassment”. The data collected was analysed using One-way Analysis of Variance (ANOVA). The results of the findings revealed that there is no significant difference in the causes, consequences and strategies for curbing sexual harassment in tertiary institutions in Kwara State. Based on the findings, this study recommended, among others, that there should be an orientation course to sensitize students on their rights and obligations within which issues of sexual harassment must be featured; that tertiary institutions should provide frameworks that would allow for staff and students to dialogue on the issue of sexual harassment; and that higher institutions should have a written policy for disciplining any erring staff and students on matters bordering on sexual harassment. Schools in conjunction with a student affairs unit should, from time to time, organize workshops on the evils of sexual harassment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".