Academic Integrity Through a SoTL Lens and 4M Framework: An Institutional Self-Study
Bibliographic record
Abstract
Abstract Institutions are placing increased emphasis on the importance of academic integrity. Suffusing a culture of integrity is complex work. Influencing academic cultures (including the shared norms, values, behaviours and assumptions we hold) requires impact across multiple organization levels, stakeholders, structures and systems. These dimensions can be influenced by working with individual instructors, learners and staff (micro), across departments, faculties, networks and working groups (meso), through to the institution (macro), and disciplinary, national and international levels (mega). Akin to nurturing strong teaching and learning cultures communities and practices, institutions tend to support change at the institutional (vision, policies, structures) and individual levels (targeted programs to develop expertise). Less focus has been placed on how we establish strong networks of support and knowledge-sharing to influence decision-making, action, and change at the meso and mega levels. In this chapter we offer an institutional self-study of academic integrity through a scholarship of teaching and learning (SoTL) lens. Informed by the 4M (micro, meso, macro, mega) framework, we examine how integrity is upheld and enacted at each level. We examine both formal and informal approaches to academic integrity, looking at how a systematic, multi-stakeholder networked approach has helped to establish a culture of integrity at our institution, and make recommendations for others, wishing to do the same.
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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 | 0.051 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| 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".