Academic Integrity Leadership and Community Building in Canadian Higher Education [Keynote address]
Bibliographic record
Abstract
Keynote address for ICAI Canada 2024. In this presentation the authors present highlights from the collaborative book chapter with the same title. The authors showcase the development of academic integrity leadership in Canada is examined through the lenses of asset-based community development and strengths-based leadership. Examples are provided from international, national, and regional perspectives that highlight how the work of promoting academic integrity can be undertaken successfully in a geographically large country with a decentralized system of higher education. A core argument of this chapter is that a strengths-based approach to academic integrity community development and leadership can be effective in situations where top-down support (e.g., federal ministry of education or a national quality assurance body) is lacking. Cite as: Eaton, S. E., Stoesz, B. M., & McKenzie, A. (2024, March 7). Academic Integrity Leadership and Community Building in Canadian Higher Education [Keynote address] International Centre for Academic Integrity (ICAI) Annual Conference 2024: ICAI Canada Day, Calgary, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 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; both teacher heads agree on what is shown here.
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".