A Layered Approach to Critical Friendship as a Means to Support Pedagogical Innovation in Pre-service Teacher Education
Bibliographic record
Abstract
In this article we describe and interpret how two distinct layers of critical friendship were used to support a pedagogical innovation in pre-service teacher education. The innovation, Learning about Meaningful Physical Education (LAMPE), focuses on ways to teach future teachers to foster meaningful experiences for learners in physical education. Critical friendship was applied in two ways: (1) the first two authors served as critical friends to each other as they taught their respective teacher education courses using LAMPE, and (2) the third author acted as a meta-critical friend, providing support for and critique of the first two authors’ development and enactment of the innovation. Over two years, data were gathered from reflective journal entries, emails, recorded Skype calls, and teaching observations. The two layers of critical friendship held significant benefits in advancing and supporting the development of the innovation while also contributing to the professional learning of all participants. Analysis of the first year’s data showed that we entered the critical friendship without thoroughly considering what we each hoped to give and take from the relationship or acknowledging the potential problems that might unfold. In the second year, guided by suggestions from our meta-critical friend, we took a more rigorous inquiry stance as critical friends, contributing contentious feedback and pushing each other beyond our personal and pedagogical comfort zones. This led to a noticeable improvement in our professional learning about teacher education practices and advanced the development of the LAMPE innovation.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".