Horizons of Self-Composure: Using Poetry and Scribble-Portraits to Explore an Aesthetics of Care / Horizons de la maîtrise de soi : poésie et portrais dessinés pour étudier l’esthétisme de l’attention
Bibliographic record
Abstract
Abstract: We are teacher-educators who use poetry and drawing as ways to evoke the aesthetic dimensions of care and to encourage the expression of vulnerability and perspective-taking in ourselves and ultimately within our classrooms. This piece describes an auto-ethnographic process surrounding the exploration of teacher-writer identity through the use of “scribble-portraits” and poetry. We created self-portraits and shared poetry related to our conceptions of ourselves and of our personal and professional identities. Central insights related to how poetry and image allow for dialogue and reflection related to a productive space where new identities and aesthetic experiences can enhance the practice of teaching and learning.Keywords: Auto-ethnography; writing identity; self-portrait; academic writing; imagery and the symbolic.Résumé : Nous sommes des formateurs d’enseignants qui utilisons la poésie et le dessin pour évoquer les aspects esthétiques de l’attention et pour promouvoir l’expression de la vulnérabilité et de la coordination des points de vue, d’abord par rapport à soi puis à nos étudiants. Cet article décrit le processus auto-ethnographique entourant l’exploration de l’identité de l’enseignant-rédacteur par le biais de « portraits dessinés » et de poésie. Nous avons créé des autoportraits et partagé de la poésie en fonction de notre conception de nous-mêmes et de nos identités personnelles et professionnelles. Aperçu fondamental de la façon dont la poésie et l’image nourrissent le dialogue et la réflexion en fonction d’un espace productif dans lequel nouvelles identités et expériences esthétiques peuvent améliorer la pratique de l’enseignement et l’apprentissage.Mots-clés : auto-ethnographie, identité rédactionnelle, autoportrait, rédaction universitaire, imagerie et symbolisme.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".