Building Community: Creating Faculty/Staff - Student Partnerships at a Canadian Applied Learning College
Bibliographic record
Abstract
Building a community of integrity in educational institutions requires the support of all its members (Eaton, 2022). Inspired by Freeman et al. (2014) the students as partners (SaP) movement is one initiative toward building academic integrity community (as cited in Lancaster, 2022). The SaP practice seeks to “engage students and staff as collaborators on teaching and learning endeavours, establishing collegial working relationships based on reciprocity, mutual respect, shared responsibility, and complementary contributions” (Marquis, Black, & Healey, 2017, p. 720). Co-designing and co-facilitating in academic integrity endeavours has the immense potential to promote ownership, autonomy, engagement, and authenticity for learners, conditions that may lead to integrity violations when absent (Bretag et al., 2019). Cultivating partnerships among faculty/staff and students then is intended to prevent academic integrity breaches such as contract cheating (Lancaster, 2022) and other violation behaviours and through relationship building, may positively impact the sense of belonging, wellness, and equity for community members (McNeill, 2022). For institutions such as applied learning colleges the timeframe to engage learners in collaborations toward community building is noticeably short, ranging from 8 to 24 months, and programs are intense. In this poster presentation, learn how one Canadian applied learning college is forming faculty/staff-student partnerships to help build a community to support integrity in the classroom and beyond.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.061 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".