Educating Perfinkers: How Cognitive Tools Support Affective Engagement in Teacher Education
Bibliographic record
Abstract
Our intention is to share our lived experiences as educators of educators employing Imaginative Education (IE) pedagogy. We aim to illuminate IE’s influence on our students’, and our own, affective alertness, and to leave readers feeling the possibility of this pedagogy for teaching and learning. Inspired by the literary and research praxis of métissage (Chambers et al., 2012; Hasebe-Ludt et al., 2009; Hasebe-Ludt et al., 2010), we offer this polyphonic text as a weaving together of our discrete and collective voices as imaginative teacher educators. Our writing reflects a relational process, one that invites us as writers and colleagues to better understand each other and our practices as IE educators (Hasebe-Ludt et al., 2009). It also allows us to share with other practitioners our struggles, questions, and triumphs as we make sense of our individual and collective praxis: how IE’s theory informs our practice, and how our practice informs our understanding of IE’s theory. This text, like IE’s philosophy, invites heterogeneous possibilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".