Bibliographic record
Abstract
Recent data on academic misconduct shared by some Canadian post-secondary institutions have reported that the numbers have doubled (CBC News, 2020; CTV News Regina, 2021) or increased significantly by up to 38% (UCalgary News, 2020). These instances establish academic integrity as a current and critically important topic for institutions as well as the scholarship of teaching and learning. Discussions in this ethical area of concern focus on ways to convince students “to behave as honest and responsible members of an academic community” (UBC, Academic Honesty and Standards) during an emergency situation (such as, the pandemic) and avoid disciplinary action. Researchers in academic integrity have noted that it is essential that students are given ample opportunities to understand the concept. In this presentation, we, two undergraduate students and an instructor: (i) share some of the ways in which teaching and learning practices changed in an online composition studies classroom; (ii) discuss how these changes addressed the expectations of academic integrity; and (iii) showcase an example from a university-wide contest on academic integrity as an opportunity to remediate personal understanding of the topic and contribute towards a community service initiative.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".