Encouraging Academic Integrity Through a Preventative Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.006 | 0.053 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it