"I Learned Nothing from Him...". Reflections on Problematic Issues with Peer Modeling in Rehabilitation
Bibliographic record
Abstract
Peer learning involves processes whereby inexperienced persons learn from persons with more experience. Previous research has shown the benefit of peer learning to the rehabilitation process of people with spinal cord injuries and others using a wheelchair, yet discussions of problematic aspects are scant. Thus, the purpose of this article is to highlight two problems with peer learning. By presenting a vignette elaborated from a phenomenologically oriented case study of a wheelchair skills program at a Norwegian rehabilitation unit, the problem of a naive view of empathy and the danger of inflicting symbolic violence are reflected upon. These reflections, though tentative, draw attention to the ethical responsibility of rehabilitation professionals who use peer learning as a part of their intervention.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.060 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".