Investigating academic integrity in the Werklund School of Education: Process, policy and perceptions
Bibliographic record
Abstract
This project investigates how instructors, teaching assistants (TA’s) and administrators perceive and act upon cases of plagiarism or in the Werklund School of Education (WSE). Academic dishonesty continues to present a major problem in higher education (Altbach, 2015; Colella-Sandercock & Alahmadi, 2015; Leonard, Schwieder, Buhler, Beaubien Bennett & Royster, 2015). The WSE “Process for Reporting and Responding to Plagiarism” will be used as a tool to engage participants in focus groups and interviews to facilitate dialogue on the topic of academic integrity. REB Certificate #: REB16-1828 Name of Funding Agency: University of Calgary Scholarship of Teaching and Learning Grant Title of the Project: Academic Integrity Online: Developing Support Mechanisms for Online Graduate Students to Understand Plagiarism Amount: $10,000 CAD Project dates: 2016 - 2018
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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 | 0.053 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".