Pedagogy of care in intercultural approaches to languages education
Bibliographic record
Abstract
Disruptions caused by the COVID-19 pandemic revealed in new ways the vulnerabilities of students’ sense of self, especially in the contexts of intercultural orientations to language education where critical self-reflection can be disorienting for learners. We propose a pedagogy of care to manage such decentering, aiding the formation of caring communities of practice and development of student flourishing. Our data derives from a teacher education course on intercultural orientations to language education and mediation, taught virtually in 2020. We analysed email exchanges between the professor and students and transcribed Zoom class sessions to examine the instructor’s pedagogy based on Noddings’s ethics of care. We identify six components within the instructor’s pedagogy: relationship; motivational displacement; person in context; flexibility; engrossment; mentality of care. Students’ expressions of reciprocity suggest possibilities for their own adoption of care and flourishing. We propose that a caring and compassionate approach to pedagogy can be instrumental in mediating a space where students might develop not only intercultural competencies but also a positionality of care.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".