Bibliographic record
Abstract
This talk traces the connections between the unethical use of algorithms, inattention to issues of equity and access, and failures of data privacy to the rise of contract cheating. The reported experiences of instructors and students tell us that contract cheating firms mine student data and exploit existing relationships between students and their educational technologies in order to find new clients and to extort the ones they already have. These companies use algorithmic searches of social media to track down vulnerable students, and once granted access to a closed educational context like Moodle, approach more students in the course or institution, which is how the use of these services seems to multiply by orders of magnitude within an institution. Once these companies have student ID and credit card information, they often engage in financial exploitation of students. Research demonstrates that many of the educational conditions that drive students to seek out contract cheating firms — lack of guidance on assignments; high-stakes assessments without appropriate scaffolding; personal or financial crises — are also conditions that do not promote learning. This talk argues that the epidemic of contract cheating can be insulated against by a renewed attention to ethical pedagogical strategies in the deployment of educational technologies. Given the explosive growth of the contract cheating problem and the huge money it makes for unethical players, it is imperative that post-secondary institutions protect students by all possible means. Limiting for-profit vendor access to student data, avoiding course-in-a-box homework system approaches to education, and using open pedagogical strategies to design persistent, non-disposable assignments are critical strategies in the fight against contract cheating, as is educating students and faculty about the importance of data security and privacy.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".