Contract cheating and academic literacies: Exploring the landscape
Bibliographic record
Abstract
The phenomenon of contract cheating in higher education is complex and a growing concern for those invested in the delivery of high-quality education and positive educational outcomes. This research explores the academic literacies practices, exchanges, and tensions between and among academic ghostwriters, students, and teaching staff, within the Canadian post-secondary sector. Dominant approaches to academic misconduct have not addressed the institutional and sociocultural factors underlying the growth of the contract cheating industry. Academic institutions lacking a coherent, holistic strategy will continue to undermine the public confidence, and teaching and learning frameworks. Given the dominance and prioritizing of reading and writing in higher education, this study adopts an “academic literacies” conceptual approach (Lea & Street, 1998), which foregrounds literacy as a social practice (Street, 1984). This approach provides a lens that includes the social, cultural, and institutional, while supporting a critical gaze on the literacy practices of students, ghostwriters, and teaching staff. This qualitative study used a multiple case studies orientation, and conducted semi-structured interviews with 11 ghostwriters, 7 students, and 8 faculty members to provide the data for analysis. Texts such as advertisements, and sample ghostwritten assignments were also considered. Through the rigorous coding of literacy social practice building blocks (e.g., practices, events) (Hamilton, 2010) the data were used to create a “Literacies Landscape Map.” This map explored the “scape” (Appadurai, 1990), or “eduscape” (C. Luke, 2006) of how multiple literacies are practised and reflected in the phenomenon of contract cheating. In addition, drawing from Bourdieu’s (1972/1977, 1979/1984, 1983/1986, 1984/1988) notions of capital, habitus, and field, the study revealed findings around the practices of collaboration and literacy brokering, arguing that some students and ghostwriters are engaging in “assessment brokering” within a “fourth space” (Thacker, in press-a). This study offers a more nuanced view of contract cheating, finding a tension of exchange beyond the financial, and having implications for higher education policy. The study concludes with a call for academic institutions to consider the development of transformative, institutional strategies to support academic integrity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".