Insights on Academic Integrity Policy Development: Crafting Policy Catered to Your Institution
Bibliographic record
Abstract
The purpose of this session is to highlight the opportunities and challenges of crafting an academic integrity policy that is responsive to an institution’s unique needs and character. A robust and comprehensive policy is crucial to upholding the values and principles of academic integrity within higher education. Over the course of two years (2018-2020), Camosun College’s Office of Education Policy and Planning worked with stakeholders from across the college to develop its new academic integrity policy and procedures. The work led to an extensive overhaul of the college’s academic integrity policy along with a review of its associated procedures intended to address and appeal allegations of academic misconduct. The end result is a clear policy and set of procedures that appropriately balances the rights and responsibilities of students, faculty, and administration. The presentation will focus on sharing strategies on how to engage institutional stakeholders in a meaningful way to develop an academic integrity policy for your college/university. Emphasis will also be placed on what supports and resources are required to implement an academic integrity policy and insights from how policy implementation is going so far at Camosun.
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 | 0.064 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.061 | 0.056 |
| Scholarly communication | 0.059 | 0.023 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.025 | 0.029 |
| Insufficient payload (model declined to judge) | 0.013 | 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".