It Takes a Village
Bibliographic record
Abstract
Members of the Alberta Council on Academic Integrity (ACAI) Contract Cheating Working Group will discuss how the collective, multi-institutional nature of their work has helped to provide practical interventions for contract cheating (Clarke & Lancaster, 2006), addressing in particular this mode of misconduct's multilateral and predatory nature. Student academic misconduct in post-secondary education and research has predominantly been understood to be perpetrated by individuals undertaking unilateral action (Eaton et al., 2019). We can observe this, for example, in how motives for academic misconduct tend to be studied in psychological, sociological, or criminological terms (e.g. Rundle et al., 2019) while the ‘supply side’ (Medway et al., 2018) and structured nature (Grue et al., 2021) of contract cheating requires more exploration. Contract cheating undermines the expectation of unilateral action by virtue of its multilateral (i.e. contractual) nature, involving networks of suppliers and consumers, thereby complicating the relationship between the perpetrator and the act of misconduct and frustrating efforts to make meaningful interventions. Addressing contract cheating takes a village. Through the lens of diversified roles and from the perspectives of multiple post-secondary institutions, panel members will discuss how to engage contract cheating collectively, provide specific and concrete projects they have collaboratively undertaken to address contract cheating issues – including videos and other digital resources – and discuss recent, instructive contract cheating cases. Participants will have access to situated insights, experiences, and resources to take away and use as-is or adapt to their own needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".