Negotiating the Maze of Academic Integrity in Computing Education
Bibliographic record
Abstract
Academic integrity in computing education is a source of much confusion and disagreement. Studies of student and academic approaches to academic integrity in computing indicate considerable variation in practice along with confusion as to what practices are acceptable. The difficulty appears to arise in part from perceived differences between academic practice in computing education and professional practice in the computing industry, which lead to challenges in devising a consistent and meaningful approach to academic integrity. Coding practices in industry rely heavily on teamwork and use of external resources, but when computing educators seek to model industry practice in the classroom these techniques tend to conflict with standard academic integrity policies, which focus on assessing individual achievement.
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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 | 0.213 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.041 | 0.088 |
| Scholarly communication | 0.064 | 0.053 |
| Open science | 0.007 | 0.061 |
| Research integrity | 0.012 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".