Scaffolding the Learning Opportunities: Academic Integrity at Douglas College
Bibliographic record
Abstract
Douglas College has recently developed a wrap-around (multi-touchpoint) model of student education for academic integrity at the college. This presentation will share the preliminary work being done, both college-wide and discipline-specific, including a recent policy language update to encourage faculty to consider whether an academic integrity issue might be regarded as a learning opportunity, rather than a violation. It will also provide a snapshot of preliminary data collected about students being reported in one of the divisions at Douglas. The key educational points for students include opportunities for learning, both mandatory and optional: academic integrity workshop at orientation completion of an academic integrity module on blackboard for all new students new tailored learning modules for Arts and Business programs resources and in-class instruction from the library tutoring sessions from the Learning Centre with peer tutors a new student-facing website (FAQ) The first contacts for learning are within the students first few weeks and organized by the college, program or instructor, while the Learning Centre sessions are directly booked by students by selecting from three academic integrity topics: Understanding and Awareness, Using Sources in Your Writing, Style and Formatting Guidelines. In these appointments, students build on previous understanding of academic integrity, learn about resources available, and develop plans to continue their learning. Writing appointments also aid students to focus on developing paraphrasing skills and ability to use sources as evidence in writing assignments, how to find the formatting and style rules needed for assignments, as well as what aspects of the style and formatting are important in most college assignments. We’ll also outline possible next steps at Douglas, including understanding learning effectiveness and increased collaboration between Faculties.
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.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".