Academic integrity in Canada: An author panel for this important new book
Bibliographic record
Abstract
“Academic integrity in Canada: an enduring and essential challenge” is an open-access book recently published by Springer as part of the series “Ethics and Integrity in Educational Contexts”. Edited by scholars Sarah Elaine Eaton and Julia Christensen Hughes, it contains over 600 pages in 31 chapters designed to address the gap in Canada’s study and evidence-based recommendations involving academic integrity. The book is divided into five sections: Canadian context, emerging and prevalent forms of academic misconduct, integrity within specific learning environments and professional programs, barriers and catalysts to academic integrity: multiple perspectives and supports, and institutional responses. In this moderated panel session, several chapter authors as well as Sarah Elaine Eaton will delve into specific aspects of their contributions towards the book. Through the open question and discussion segment, attendees will be inspired to contribute in their own institutional roles towards provincial and, ultimately, the growing Canadian academic integrity community. Learning Outcomes Discuss academic integrity culture and initiatives in Canada Identify ways to make contributions towards academic integrity in various roles and institutions Reflect on specific aspects of academic integrity covered in individual chapters
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".