Encouraging Academic Integrity Through a Preventative Framework
Bibliographic record
Abstract
Through a collaboration between the Teaching and Curriculum Development Centre (TCDC), the Centre for Intercultural Engagement (CIE) and the Academic Integrity and Student Conduct Office, Langara has developed an open access toolkit for educators called “Encouraging Academic Integrity Through a Preventative Framework”. The impetus for developing a toolkit focused on encouraging academic integrity came from increasing requests for support in addressing the challenges of academic misconduct at our institution. This toolkit was developed to provide instructors with methods and examples of activities and assessments that can help students meet academic standards and expectations. This document is divided into four parts: we start with an exploration of the principles of academic integrity as defined by the International Centre for Academic Integrity, and then move on to examine the complexity in expression and perception of academic integrity using a model we call the complexity quadrant. With this model in mind, we discuss strategies for fostering integrity and preventing contraventions of academic integrity standards through the use of different assessment design practices. We propose to present the sections of the toolkit, focusing on the complexity quadrant, using an interactive discussion approach. By the end of the presentation, participants will be able to: Use the complexity quadrant to reframe conversations around academic integrity Describe assessment design practices that encourage academic integrity The e-book is available for free through BC Campus Pressbooks Open Education Resources.
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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 | 0.147 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.029 | 0.087 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.008 | 0.043 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".