Bibliographic record
Abstract
While academic integrity as a specialised profession in higher education is still emerging (Vogt and Eaton, 2022; Mackenzie, 2024), learning developers (LDers) perform many duties to teach and cultivate academic integrity at their institutions. As Bickle, Allen and Mayer (2023, p.1) highlight, many LDers ‘have designed and delivered courses, quizzes, tutorials, and events to promote academic integrity’ and encourage ethical scholarly practices. At their ALDCon23 session about the role of learning development (LD) in academic integrity, Bickle, Allen and Mayer posed questions that are still pressing today, such as, ‘what training do learning developers need?’ and ‘what forms of collaborative cross institutional research on academic integrity would be advantageous?’. This poster (see Figure 1) responds to Bickle, Allen and Mayer’s session by sharing reflections on a new service our LD team launched in 2023 in partnership with our student conduct office. At our Canadian institution, instructors who report academic misconduct must select one or more ‘resolutions’, and a one-to-one meeting with an LDer is now one option. As LDers have no impact on institutional decisions around misconduct, we have attempted to create a neutral and safe space in these meetings for students to share their experience, deepen their understanding of academic integrity, and develop strategies to help them move forward more confidently in their studies and in contexts beyond higher education. While this model of LDer support is not brand new (Bridgewater, Pounds and Morley, 2019), it remains uncommon in Canada and is worthy of further exploration.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.188 | 0.063 |
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".