Bibliographic record
Abstract
After several years of developing a culture of academic integrity at Assiniboine Community College, over twenty stakeholders from five college campuses and a dozen different service areas and academic programs formed the Academic Integrity Advisory Committee (AIAC) in late 2019. In the midst of a push through emergency remote learning, and towards blended learning ahead, they undertook a multi-stage plan: to develop, implement, and evaluate a revised academic integrity policy. Using research and evidence from the world’s foremost sources to inform their work, the AIAC embodied Assiniboine’s academic signature of learn by doing. Join members of the AIAC for a detailed look at the ins and outs of this policy revision process. Participants will leave this session with an understanding of several key academic integrity frameworks, how to implement a change model at their own institution, and a deeper understanding of how to collaboratively develop an academic integrity policy.
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 | 0.230 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.067 | 0.037 |
| Scholarly communication | 0.052 | 0.025 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.048 | 0.060 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".