Forms of Dishonesty Amongst Academic Staff and the Way Forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it