Bibliographic record
Abstract
Academic misconduct is typically recorded on student files to deter cheating and identify repeat offenders, but how often is this data analyzed to find systemic issues? By collecting student data, Prinsloo and Slade (2017) assert that higher education has a duty to act in creating improved student experiences. Teymouri et al. (2022) propose the use of academic misconduct data to identify gaps in student supports, policy education, and assessment design. This session will apply a systems thinking approach to propose that post-secondary institutions can create institutional responses in support of academic integrity based on findings from collected academic misconduct data. Reallocating the intensive energy required to respond to misconduct toward early education and assessment design benefits both staff and students. Literature supporting a systems thinking approach for effective data use will be discussed, as well as examples from practice at Conestoga College. Attendees will gain frameworks for applying systems thinking and ethical data use, as well as the opportunity for questions and further discussion.
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 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 | 0.058 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.022 | 0.098 |
| Scholarly communication | 0.032 | 0.021 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".