Bibliographic record
Abstract
This book explores Aristotelian and Confucian wisdom traditions to understand education and what counts as a good teacher in an embodied dialogic approach. The book creates a dialogue between ancient ideas and the author’s lived experiences as a teacher in cross-cultural landscapes today to ruminate on the important themes of educational purpose, teacher excellence, teacher-student relationships, and teaching skill. It asks fundamental educational questions including "Why Do We Educate? Eudaimonia and Dao"; "What Do We Educate? Phronesis, Philia and Ren"; and "How Do We Educate? Techne and Liuyi". Moving beyond the dominant epistemological concerns such as how to teach more effectively to help students gain better marks in schools, it constitutes an ethical inquiry that illuminates the values, purposes, concerns, and hopes that animate genuinely educational work. Using a comparative approach to wisdom traditions from both the East and the West, it addresses parochialism and challenges Eurocentric research paradigms. Embedded in the messy ground of teaching in intergenerational and cross-cultural narratives, the author’s own experiences as a student/teacher/daughter of a teacher/mother of a student crucially unpacks and concretizes ancient concepts and reactivates them in concrete situations. A sense of a whole without completeness, a conception of the good without closure, and an aspiration without achievement continue to haunt the search for an ultimate answer to the question "what counts as a good teacher?". It will appeal to scholars, teachers, and teacher educators with an interest in narrative inquiry and educational research, as well as those in the field of curriculum studies and the philosophy of education.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.071 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".