From Care Ethics to Care Writing: Multimodal Literacies for Expanding Care in Complicated Times
Bibliographic record
Abstract
This article focuses on research into care writing, as we took it up in collaboration with teacher-researchers in three high school English language arts classrooms in Edmonton, Alberta, Canada. Drawing on feminist and posthumanist perspectives, the research engaged high school teachers and students in “complicated conversations” ( Pinar, 2019 ) about care that is oriented towards self, others, and the world in relation to societal, cultural, historical, and political contexts. By conceptualizing care writing in collaboration with students and teachers, the article explores an aesthetic and educationally entangled enactment of care ethics researched through multimodal literacy activities. In doing so, care writing became care creation, fostering social and emotional learning, well-being/becoming, and a sense of responsibility and relationality with and beyond humans. This article aims to develop these preliminary conceptualizations by foregrounding one teacher-researcher’s experience of naming and engaging her students in the concept of care through writing processes and practices, including multimodal and multilingual research-creation, in her classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.119 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".