Academic Integrity Pledges -- Acculturating Students to Integrity within Canadian Higher Education
Bibliographic record
Abstract
Academic integrity, and its associated values, are an essential aspect of cultures that provide a foundation for conduct and behaviour of all members within higher education. Establishing such cultures support the quality of student learning as well as their ethical deportment within their programs of study. The merits of acculturating students within academic integrity has implications for their performance and commitment to similar conduct and behaviours in their chosen professional fields as positive, ethical, and caring professionals benefitting all who receive their care and service. Creating and sustaining such learning cultures requires a multifaceted approach and an understanding and appreciation for the complexity of nurturing such environments. Academic integrity pledges situated across the learning trajectory at meaningful times during students' developmental paths, serve as one strategy that can be effective to academic integrity efforts. How one School of Health Sciences has approached and realized academic integrity pledges are discussed, and may serve as an example for others. The successes and opportunities for future development are outlined and reviewed.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".