Poetry unveiled: Multimodality and aesthetic responses as a fresh approach to teaching and reading verse
Bibliographic record
Abstract
Abstract In secondary English classrooms, poetry is often a text that is least liked because it is viewed as being “inaccessible,” reserved for the elite, and/or too abstract. Part of the reason for this also lies in the traditional, colonial structures of introducing poetry such as relying on canonical texts and close reading analysis. Yet, outside of the classroom poetry is used in more accessible and engaging manners. With the advancements of technology, there also includes multimodal ways in which to read and write poetry that could be much more interesting for both educators and youth. This paper opens a discussion to consider multimodal and aesthetic responses to including poetry such as using digital apps like PhoneMe, a free accessible platform that allows users to post their written poems, record themselves reciting poems, and pin their poems directly on to an interactive digital map. The uniqueness of PhoneMe—a layered multimodal approach—can provide a more engaging way to teach and learn poetry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".