Academic Advisors as Valuable Partners for Supporting Academic Integrity
Bibliographic record
Abstract
Academic Integrity is a fundamental value in higher education. Due to the increased ease of access to all types of information through social media and the internet, the lines have become blurred on what is can be “borrowed” and used. Recent proliferation of contract cheating has only reinforced that integrity cannot be just the responsibility of the Dean’s Office or the Academic Integrity offices. Advisors and learning strategists who see students regularly, can ubiquitously play a valuable role in integrating academic honesty into their conversations and workshops. This can be achieved in collaborations with campus partners on campus wide programming, starting early with integrating the conversation about honesty in academic orientations for new students and parents, and when having difficult conversations about study success and academic decision-making.
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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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.039 | 0.021 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".