Increasing diversity in peer-to-peer education: A case study of manager experiences with student paraprofessionals in learning development in the Canadian context
Bibliographic record
Abstract
This autoethnographic case study examines the experience of managers with hiring student paraprofessionals into various roles within peer-to-peer education models and programmes as a method to increase the diversity in learning development services in the Canadian context. Tailoring learning development through peer-to-peer education models for diverse student groups is an important aspect of how learning development supports students in higher education. Including the knowledge and perspectives of student paraprofessionals who better reflect the diversity of the population we serve has been an important aspect of our practice. Our purpose for this case study is to better understand how our experiences with paraprofessional staff diversity, over a seven-year period (2010-2017), have influenced our practice of learning development in an institutional context focussed on creating a more inclusive and welcoming environment on campus to better support the needs of diverse learners. The knowledge that we gained through this analysis of diversity and peer learning as an approach to learning development may serve as an example of the value of autoethnography as a method to provide useful insight to professionals and leaders in the field.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".