Looking through the Assurance Lens: Institutional Governance of Academic Integrity Strategies
Bibliographic record
Abstract
Academic integrity is central to the reputation and mission of a university and the credibility of the awards it confers. The need for a comprehensive academic integrity strategy is well understood, but the role of governing bodies in assuring institutional integrity is less well-defined. As part of their high-level oversight, higher education governance bodies are accountable for institutional risks, including assurance of quality and standards. Academic dishonesty can seriously undermine quality and standards, and lead to reputational damage. Therefore, academic integrity should be a priority for governing bodies. This article proposes a comprehensive regime, using a maturity matrix, to provide assurance of inputs, outcomes and impacts relating to academic integrity. Types of evidence are presented that could be used to underpin an assessment of maturity using the matrix, with discussion of how the outputs from this assessment might be used to uplift institutional responses to academic integrity.
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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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".