Ethics, EdTech, and the Rise of Contract Cheating
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
Abstract This chapter argues that establishing a “culture of academic integrity,” in the era of digitally-situated plagiarism like contract cheating, begins with an institutional approach to student data and student work that is rooted in ethics. If “students cheat when they feel cheated” (Christensen Hughes, 2017, p. 57), then the ethical failures inherent in a system-wide move toward for-profit homework systems and plagiarism checkers sets a dangerous model for students to follow. We are responsible for modelling for our students what it looks like to be a contributing member of an academic community, and we do so by taking seriously our students, their data, and their work, and not only when it comes time to run it through a plagiarism detector or check their IDs against a proctoring software. This chapter argues that a more responsible relationship to student data, and a less cozy relationship with for-profit educational technologies, is required if our institutions are serious about fostering a culture of academic integrity.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.028 |
| 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 it