Bibliographic record
Abstract
This study sets out to investigate the forms of academic dishonesty prevalent among academic staff and the reason for their prevalence. The study used academic staff in two tertiary institutions in Cross River State, Nigeria. The survey research design was adopted. Three research questions guided the study. A questionnaire was developed, face validated and used for data collection from a convenient sample of 105 academic staff. Findings show that collectionof money to change grades for students, inclusion of name in a published paper one did not contribute to, taking adjunct lectureship in more than one place at a time and covering up examination malpractice cases are some examples of the academic dishonesty exhibited by the teaching staff. Desperation for promotion, get rich quick mentality and corruption in the society, laxity in punishing “culprit” lecturers and pressure from students and their parents or guardians were cited as contributory factors to the prevalence of academic dishonesty amongst the teaching staff. Suggested strategies for curbing the menace include ethical re-orientation seminars for academic staff, proper supervision of academic staff by heads of departments and appropriate sanctioning of guilty lecturers. Key words: Academic dishonesty; Academic staff; Prevalence; Academic integrity; Moral value
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".