Academic Integrity: Faculty Development Needs for Canadian Higher Education - Research Project Brief
Bibliographic record
Abstract
This collaborative project includes researchers from four Canadian universities (University of Calgary, University of Guelph, University of Manitoba , University of Waterloo), as well as partners from D2L. This is the inaugural project associated with the D2L Innovation Guild. The purpose of this research is to understand faculty perceptions and needs related to academic integrity in Canadian higher education. This mixed-methods study will include a survey to be administered to faculty at four Canadian institutions. The survey designed for this research project will be informed by previous research (i.e. McCabe, 1993). The first stage of this project includes a detailed literature review on faculty perceptions of academic integrity in Canada and globally. This literature review is meant to inform the development of a survey tool and the research methods. The survey tool will be designed to capture qualitative and quantitative data based on the faculty responses about academic integrity in Canada. The goal of this research is to explore and better understand how faculty members in Canadian higher education institutions support academic integrity. Key words: academic integrity, faculty, plagiarism, post-secondary, higher education, Canada
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".