Bibliographic record
Abstract
An unanticipated move to remote teaching and learning in post-secondary institutions in March 2020 in response to the pandemic, left many of us scrambling to adapt our course content, teaching practices, and assessments to the online environment. On top of this, we, as educators, began to grapple with questions and realities regarding how the online landscape presented new challenges and opportunities related to academic integrity. Whatever academic integrity vulnerabilities and concerns that existed in our face-to-face offerings amplified when we went remote leaving many of us to implement makeshift adjustments to our courses and assessments to ‘close the holes.’ Academic integrity, however, should be built into curriculum development and teaching pedagogy rather than a situational response. Such an approach ensures that all aspects of instruction and assessment arc toward supporting student learning and promoting instructor and student fairness, honesty, trust, and responsibility (ICAI, 2021). This session outlines how an instance of student misconduct early in my academic career resulted in a journey to learn more about why students engage in dishonesty, strategies to better support student learning, and practices to cultivate an educational experience that seeks to model the values of academic integrity.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.062 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 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".