Discussing Metaphor as a Pedagogical Practice: Reflections on a Conference Salon
Bibliographic record
Abstract
In this paper, we reflect on our process of preparing for a salon presentation at the York Graduate Students in Education re: conference. We describe our choice of a salon format to open a critical and interdisciplinary dialogue about the ways we struggle with metaphor as white settlers. We share the content of our salon discussion and explore how metaphor can manifest as an act of transformation in early childhood education. Specifically, we conceptualize our metaphor of early childhood praxis as a Frankensteinian monster, pieced together from mostly psychological theories of child development into a harmonized body of knowledge. As we describe our preparation for the salon, we attend to our inspiration for engaging in aesthetic invitations for dialogue through creative writing, spoken word, mixed media collage, and audio/visual recording. We end with an invitation for continued dialogue on the use of metaphor and its cautions.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.045 | 0.027 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.015 | 0.042 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".