Peer Helpers in Community Engaged Learning (CEL): Reflections on a Pilot
Bibliographic record
Abstract
In the Winter 2011 semester, a Community Engaged Learning (CEL) Peer Helper Unit (consisting of two undergraduate students) was piloted in one section of a third year sociology and anthropology undergraduate qualitative methods course. The students used community-based research (CBR) to learn qualitative methods via experiential and service learning. The initial thought was that having students who had completed a qualitative research methods course before would provide insight to students currently enrolled in the course. The unit met with the instructor weekly and submitted reflections to an online discussion board. The intent of this reflection paper is to highlight the role the Peer Helper Unit played in facilitating the course group work and the CBR projects from the perspective of both the peer helpers and the instructor. In addition, benefits and challenges of peer helpers embedded within courses are addressed.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".