“Four of my Lecturers Including my HOD Took Sexual Interest in Me”: Factors Facilitating Sexual Harassment and Exploitation in Nigerian Higher Education Institutions
Bibliographic record
Abstract
For decades, Nigerian higher education institutions (HEIs) have featured as places where sex is used to determine who gets decent marks and who gets the opposite in what is popularly known as “sex-for-marks” in the country. It is common knowledge to Nigerians that many female students have been prevented from passing their coursework or completing their programmes of study for refusing to engage in sexual activities with their lecturers when demanded. Many male lecturers, including professors, have been dismissed, suspended, put on probation, or prosecuted for sexually harassing or exploiting female students. Available evidence suggests that this phenomenon cuts across both public and private institutions across the country although it is more common in public institutions. This current study explored this phenomenon. The qualitative approach was adopted. Both primary and secondary data were utilised. Primary data were gathered through semi-structured interviews of 22 female graduates and undergraduates of 11 HEIs. Secondary data came from 8 media outlets. The data were thematically analysed. Nine themes were developed from the data, which denoted the primary factors contributing to this menace, and these are (i) project supervision, (ii) being unique, (iii) being a fresher, (iv) tuition fee debt, (v) failed assessment, (vi) exam malpractice, (vii) missed submission deadline, (viii) failing to purchase handout, and (ix) sexual obsession. These themes were analysed in depth and supported with excerpts containing the original words of the victims while pseudonyms or fictitious names were used to replace the real names of the participants. Recommendations were made on how the authorities of HEIs can effectively address this social problem.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".