Becoming a teacher does not come that easily : Aristotle, Confucius and education
Bibliographic record
Abstract
One of my earliest memories was peering through my mother’s classroom window to watch her teach mathematics to middle school students. My mother inspired me to become a teacher and I believed that I had succeeded when I was appointed as a teacher and subsequently won district and national awards in China for teaching excellence. However, I found my “successes” unfulfilling: I felt more and more like a commander training her students for the battlefield of examinations where some would succeed and many would fall. I wondered why my mother had thrived while I floundered. My search for answers took me from Beijing to Vancouver for graduate study and the realization that I was actually searching for my practical identity, that is, an identity “under which you value yourself, a description under which you find your life to be worth living and your actions to be worth undertaking” (Korsgaard, 1992, p. 83) with associated ideals, roles and excellences (Lear, 2006). Searching for a practical identity is fundamentally a normative quest for ethical integrity; unfortunately the language currently available to describe teaching emphasizes teachers’ accountability for improving student test scores and assumes that higher scores indicate better preparation of the young for future jobs—the primary purpose of schooling. My search for a suitable ethical framework to understand what it meant to be a teacher quickly led to two of the most influential approaches in human history: Aristotle’s and Confucius’. I use their ideas about how to cultivate human virtue to create a dialogic interpretation of teaching that includes concern for educational purpose (eudaimonia and dao), teacher excellence and teacher-student relationships (phronesis, philia and ren) and teaching skill (techne and liuyi), continually testing my interpretations against my own experiences as student/teacher/daughter of a teacher/mother of a student. Despite my efforts, however, my thesis remains haunted by the sense of a whole without completeness, a conception of the good without closure and an aspiration without achievement—an aporia that, following Kierkegaard (1854) and Lear (2011), I now understand as the inevitable irony of any quest for ethical closure.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".