From Implicit Intention to Exquisite Expression: Finding Metaphors for Who We Are and What We Do
Bibliographic record
Abstract
This article is designed to capture our musings on metaphors as we explore our own understandings of our professional identities: a philosopher-storyteller, a psychologist-poet, and a story-seeking-musician. We are teacher educators and researchers each with our own identity and sense of self. We are all women who work as colleagues, but we come from different educational backgrounds, have various research interests, and have our own unique approaches to teaching. We have discovered that during our ongoing conversations about our ‘‘professional’’ identities as teacher—educators, we have this one thread in common: when it becomes difficult to express who and what we are all about, we all reach for metaphors. As we engaged in dialogue about metaphor, we found that we could agree on a holistic and metaphorical identity that permeates all that we do, one that transforms the way we view ourselves and our work of teaching, learning, and researching.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 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".