Bildung and flow in teachers' stories of what it means to be a good teacher
Bibliographic record
Abstract
The expression a good teacher is ubiquitous both within and outside of schools. This study set out to better understand what being a good teacher means to practicing teachers. Using an interpretive methodology informed by philosophical hermeneutics, stories were gathered through one-on-one conversations with practicing K-12 teachers in response to questions such as what does it mean to be a good teacher? how do you know if you're a good teacher? do you ever feel like a bad teacher? who are your teaching role models? The stories shared by participants demonstrate an interplay between the Gadamerian (1989) concept of Bildung and Csikszentmihalyi's (1990) notion of flow. Thus, it seems that being a good teacher is not experienced as a static state, but rather as a dynamic experience which is made possible by and contributes to the ongoing growth of teachers. The participants' stories demonstrate how the construction and articulation of such narratives provided the teachers with insight into their own ongoing development ( Bildung) ; these stories also have the potential to promote deeper understanding for administrators and teacher educators as they support the work of teachers.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".