Practitioner's Approach to Academic Integrity through the Lens of Equity, Diversity, and Inclusion
Bibliographic record
Abstract
The aim of this session is to provide insight into the practical application of the principles of equity, diversity, and inclusion (EDI) to the promotion of academic integrity and management of academic misconduct through a practitioner’s lens. This perspective will present the experiences of practitioners working in both the K-12 and post-secondary environments, in which diverse student populations necessitate the implementation of elements of EDI into everyday interactions with learners. The discussion will include awareness of marginalizing factors, provision of voice, and intersectionality relevant to the lived experiences of students. Participants will come away with a renewed understanding of practical ways to apply an EDI lens to promoting academic integrity and managing academic misconduct.
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.041 | 0.083 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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".