New priorities for academic integrity: equity, diversity, inclusion, decolonization and Indigenization
Bibliographic record
Abstract
Abstract The topics of equity, diversity, inclusion, decolonization, and Indigenization have been neglected in academic and research integrity. In this article, I offer examples of how these issues are being addressed and argue that academic integrity networks and organizations ought to develop intentional strategies for equity, diversity and inclusion, and decolonization in terms of leadership, scholarship, and professional opportunities. I point out that existing systems perpetuate the conditions that allow for overrepresentation of reporting among particular student groups including international students, students of colour, and those for whom English is an additional language. I conclude with concrete recommendations for action.
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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 | 0.127 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.094 |
| Scholarly communication | 0.040 | 0.041 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.012 | 0.026 |
| 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".