Bibliographic record
Abstract
Abstract This chapter highlights the imperative for attention to, and action in, the promotion of academic integrity in work-integrated learning (WIL) settings across post-secondary programs. The importance of such efforts are closely tied to the efforts of strengthening ethical comportment with graduates who will go on to contribute to client care, client service, leadership, and research that will directly impact members of the public, hiring organizations, and global systems. WIL settings provide invaluable opportunities for students to learn essential skills and acculturate to professional ethical values through real world experiences. The experiential learning that happens in these settings helps influence the professionalization of students, encouraging safe, ethical practice that benefits those receiving care/service, future employers, and society. Since WIL is offered in both college and university settings and occurs across a number of professional and service programs, it has the potential to significantly influence a vast and varied number of professionals entering numerous career paths around the world. All members of learning communities in post-secondary organizations have a responsibility to understand their roles and opportunities in supporting, maintaining, and promoting academic integrity across WIL settings. While the narrative for the chapter is Canadian, the observations and recommendations may be relevant in other countries, where WIL plays a significant role in the education and development of professionals and service providers across a number of professions and trades.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".