Thinking/Teaching in Multiple Tongues: The Interdisciplinary Imagination
Bibliographic record
Abstract
In this interpretive paper I reflect upon my experience of working on a pedagogical initiative several years back to cultivate in secondary fine art students predispositions and attunements in the teaching of "second teachables." Beginning with the concept of fear I outline my and my students' pursuit to interpret and understand more deeply the strict and bounded nature of disciplinary subject areas and the imaginative and creative potential of interdisciplianarity. Captured by an incidental but evocative reference to multiple tongues, I describe the unanticipated places we found ourselves as we followed the historical, cultural and contemporary sense-making around multiple tongues. I consider what this might mean for our work in teacher education and our continued striving to have us think differently about curriculum and its always malleable borders.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".