Strengthening Academic Integrity in Canadian Higher Education: Implications for Alberta
Bibliographic record
Abstract
This session synthesizes information from a variety of sources to provide an evidence-informed overview of the development of academic integrity in Canada, including research, teaching and learning, policy and student affairs. This session draws in part from published research tracing advancements in the field in Canada back more than a quarter of a century (Eaton & Edino, 2018). This session highlights particular contributions from Alberta. Ten evidence-based implications emerged that can serve as a basis for deeper dialogue. Learning Outcomes By the end of this session participants will be able to: 1. Describe the development of academic integrity in Canadian Higher education. 2. Identify areas for growth in terms of advancing academic integrity in one’s own professional practice. 3. Engage in more evidence-informed dialogue about how to uphold integrity and what to do when there are breaches of integrity. Citation: Eaton, S. E. (2020, January 22). Strengthening academic integrity in Canadian higher education: Implications for Alberta. Paper presented at the Athabasca University, Faculty of Humanities and Social Sciences (FHSS) Research and Learning Symposium, Edmonton, AB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".