Bibliographic record
Abstract
Academic integrity practice and research has most often looked at action either at the individual instructor level (e.g., classroom strategies) or at the institutional level (e.g., policies). Scholars have called for attention to disciplinary patterns (Bretag et al, 2019; Rogerson et al, 2022) and for increased emphasis on the meso or middle levels of higher education institutions to influence change (Kenny & Eaton, 2022). A coordinated approach at the program level has the potential to better contextualize the values of academic integrity for students in a disciplinary or professional community, build the specific skills students need to avoid forms of academic misconduct of particular concern, and incorporate assessment approaches that translate to students' futures. A framework for assessing multiple approaches will be presented, along with potential limits and benefits of each approach. Participants will have an opportunity to situate their own examples and explore those of others. Participants can expect they will leave the session able to (1) articulate the importance of coordinating for academic integrity at the level of the program, and (2) identify a coordinating approach that they can try or advocate for in their own context.
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 | 0.051 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.048 | 0.020 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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".