Pay-to-Pass: Emerging On-Line Services that are Undermining the Integrity of Student Work
Bibliographic record
Abstract
Students have been connected digitally from an early age and have been encouraged throughout their educational journey to turn to the internet for information. However, less emphasis is typically placed on educating students about the origin and appropriateness of these sources. For many post-secondary students, it can be challenging to distinguish between resources that are supportive of their academic development and resources that are undermining and questionable in their veracity. In this workshop, we discuss the emergence and infiltration of pay-to-pass websites in the Canadian post-secondary setting. We differentiate pay-to-pass websites from other forms of contract cheating by describing them as sites encouraging students to share and access course material, assignments, tests, and notes for academic and personal gain. These sites are alluring to students because they commonly offer real-time support from academic 'experts' or tutors that is available 24/7. In the rapid shift to online learning due to the COVID-19 pandemic, we have acutely observed the impact of these services on student behaviour in online-based assessments. This workshop examines the growing scope and deepening impact of these sites on teaching and learning in the post-secondary context. To address the challenges posed by these websites, we present a three-part approach that may be implemented in the efforts to uphold academic integrity in post-secondary education.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".