Creating an Educative Approach to Academic Integrity within an Ontario College Public–Private Partnership
Bibliographic record
Abstract
Abstract\nThis dissertation-in-practice investigates the impact of limited proactive educational practices to support academic integrity at the College of Central Ontario (TCCO; a pseudonym) in a public–private partnership (PPP) with a private career college (PCC). Addressing this significant leadership problem of practice is needed, as academic integrity underpins institutional reputation, the validity of academic credentials, graduate credibility, and workforce competencies. Students’ cultural socialization and educational environment play pivotal roles in shaping their understanding of integrity and practice of ethical decision-making. Thus, international students studying at TCCO-PCC benefit from ethical leadership and tailored support to uphold expected institutional integrity standards. It is crucial for leadership to support a framework that tackles present challenges, one that emphasizes solution-driven approaches to boost integrity knowledge and education at TCCO-PCC. This requires teamwork to close gaps, ensuring the institution’s dedication to integrity and collaboration with academic partners for change. The success of this dissertation-in-practice will be aligned with both transformational and transformative leadership approaches and will focus on supportive methods to reduce academic integrity issues while providing educational opportunities to explore holistic integrity (academic, personal, and professional). This dissertation-in-practice outlines a fulsome change implementation plan, a communication plan, an outline for monitoring and evaluating the change efforts, and methods for TCCO-PCC to leverage appropriate knowledge mobilization to enhance and improve integrity education and knowledge acquisition.\nKeywords: academic integrity, holistic integrity, public–private partnership, PPP, international students, ethical leadership, ethical decision-making
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".