Bibliographic record
Abstract
Students encounter wide-ranging teaching and learning contexts and approaches in Canadian post secondary institutions. As a result, they receive and perceive mixed messages when it comes to academic integrity. The purpose of this session is to remind us, as educators/ scholars/researchers what it is like to be the “novice” in our disciplines and fields. The presenter calls for more explicit and contextual teaching of the norms and skills required for academic integrity. This will lead to discussion of (1) what should be done at the program level to explain and educate students on ethical academic and professional practices; and (2) the value and the limits of awareness campaigns and standardized syllabus statements. The session foreshadows a chapter in a forthcoming edited book about academic integrity in Canada and fits with “best practices and emerging ideas in academic integrity policy development.”
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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.072 | 0.043 |
| Scholarly communication | 0.029 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".