Education as a Financial Transaction: Contract Employment and Contract Cheating
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.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".