Building a Culture of Academic Integrity through Restorative Justice
Bibliographic record
Abstract
Restorative justice (RJ) is a philosophy and set of values and principles that can inform justice practices and responses to harm. Its processes aim to address needs, repair or transform relationships, and promote understanding, meaningful dialogue, and direct accountability. Ethical and mindful approaches to RJ processes have the potential to address power imbalances, enhance community participation, establish relationship-based environments, and respond more meaningfully to human needs, which promotes social justice. A restorative lens has been applied to post-secondary environments in relation to academic and non-academic harms that occur on and off campus between students, faculty, and staff. This workshop will describe what we are learning through taking a restorative approach to building a culture of academic integrtity at Kwantlen Polytechnic University. Workshop participants can expect us to cover the following topics:* Describe the principles and procedures of restorative justice in relation to promoting a culture of academic integrity* Identify the benefits of restorative justice as an effective response to academic integrity violations compared to punitive approaches
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.044 | 0.071 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".