YouTube: An international platform for sharing methods of cheating
Bibliographic record
Abstract
This study investigated the video sharing website www.youtube.com for the presence of instructional videos that teach students how to cheat on academic work. Videos were analysed to determine the methods of cheating, the popularity of the videos, the demographics of viewers and those uploading the videos, and the opinions of viewers after watching these types of videos. A total of 43 videos were included in this study. Those featured in the videos taught viewers how to cheat on exams, homework, and written assignments using modern and traditional technologies. The far majority of those featured in the videos, and their viewers, were males within the age range of those who attend middle school, high school, and college. Videos were watched by people from several different nations, including the United States (US), Canada, Australia, India, and the United Kingdom (UK). The study's results suggest that instructional cheating videos are popular among students around the world. Positive viewer feedback indicates that the videos have educated and motivated students to put the methods of cheating found in the videos to use. Educators should consider YouTube as a resource in order to become familiar with various methods of cheating.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".