Exploring a Curricula of Visual and Poetic Aesthetics / Exploration de programmes d’esthétisme visuel et poétique
Bibliographic record
Abstract
Abstract: In this article, I explore the role of visual arts in shaping the future direction of the literary arts in my pre-service teacher education classroom. I outline a cross-curricular curriculum by exploring a theoretical and practical relationship between visual and poetic aesthetics. Drawing upon the imagination, we are able to become critical storytellers as we engage in ekphrastic poetics, that is—a poetic response to a form of art. Ultimately, we modify and expand on the practice to include responses to photography or works of art—that are themselves aesthetic responses. Key words: Arts; Education; Imagination; Ekphrastic Poetics; Curriculum. Résumé : J’analyse dans cet article le rôle des arts visuels dans l’orientation future des arts littéraires dans le contexte de ma classe de formation initiale des enseignants. Je donne un aperçu d’un programme transdisciplinaire en analysant le lien théorique et pratique entre l’esthétisme visuel et l’esthétisme poétique. Nous pouvons, grâce à l’imagination, devenir des raconteurs critiques par le biais de la poésie ekphrasique, c’est-à-dire par le biais d’une réaction poétique vis-à-vis une forme d’art. En bout de ligne, nous modifions et élargissons la pratique pour y inclure les réactions face à la photographie ou à des œuvres d’art, qui sont elles-mêmes des interprétations esthétiques.Mots-clés : arts, éducation, imagination, poésie ekphrasique, curriculum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".