Education as a Financial Transaction: Contract Employment and 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 Over the last decade, high-profile cases of academic misconduct have surfaced across Canada (Eaton, 2020a). I argue that it is systemic issues that contribute to their ubiquity: knowledge is seen as a commodity, transcripts and credentials as products, and students as consumers. As provincial governments in Ontario and Alberta introduce funding models tied to graduate earnings and employment (Anderson, 2020; Weingarten et al., 2019), education becomes a financial transaction and academic integrity is threatened. Credentials hold more value than the process of learning, and when students pay for credentials, it is more palatable to pay for grades. This is exacerbated by a supply and demand for academically dishonest practices. File sharing websites that facilitate cheating are ubiquitous; coursehero.com alone is worth over one billion dollars (Schubarth, 2020). Targeted advertisements for essay mills abound. Meanwhile, academia increasingly relies on the labour of sessionals (Shaker & Pasma, 2018), who tend to underestimate the scope of misconduct (Hudd et al., 2009) and are less likely to report infractions (Blau et al., 2018). Furthermore, those with graduate degrees are increasing (Wall et al., 2018) while stable academic jobs are fewer (Kezar, 2013). Academics faced with precarious employment often supplement income in what Kezar et al. (2019) refer to as the “gig academy”. They are well-positioned to meet the demand for ghost-written papers (Sivasubramaniam et al., 2016). Although many institutions have responded with well-articulated policies and procedures, when entrenched in a system that incentivises and facilitates dishonest practices, they are not lasting solutions to chronic problems.
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 | 0.004 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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