Bibliographic record
Abstract
Concerns around academic integrity (AI) are a national and international focus, as academic misconduct incidents have increased in recent years. Most institutions have committees, departments and policies dedicated to addressing AI, and increasingly recognize the punitive approach as ineffective and counter-productive. Accordingly, many institutions have transitioned from a punitive and reactive approach to an educational and preventative approach, and various faculty members struggle to develop strategies supporting this approach. Restorative justice provides a strong foundation and framework for this transition. Dr. Pawlychka will outline the restorative justice philosophy and share innovative and practical strategies for faculty to use in the classroom to increase student responsibility and capacity for AI while strengthening the faculty-student relationship. Her approach to fostering a culture of academic integrity begins at course and curricula development and continues through all aspects of course delivery, in-class discussion, and instructor-student contact. She will share pedagogical methods, based on RJ philosophy, current research, professional experience, and student feedback, that have resulted in decreased academic misconduct incidents, strengthened student commitment to academic integrity, and enhanced enjoyment of both teaching and learning! Strategies presented will include tips for in-class AI discussion, redefining terminology, development of AI handouts and assignments, and restorative approaches when meeting with students. Participants are invited to bring ‘scenarios’ for 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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.032 | 0.090 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".