Bibliographic record
Abstract
Abstract The following is a qualitative portrait of a creative teacher and her teaching process. Over a period of six months, five interviews were conducted with the teacher before, during, and following a university course in teacher education on instructing diverse learners. Additional interviews were conducted with six students at the beginning and end of the course and with the teacher's husband following the course. Additional data sets include classroom observations revealed in field notes, personal memos, and course materials. The overarching themes represented constructs involving intense and thorough course preparation, teacher‐student connections, and reflective teaching. Sub‐themes guiding the process of creative teaching emerged including constraints placed on preparation and reflective teaching, an awareness of self and students within the process of preparation and connection, feedback from colleagues and students guiding the connection and reflective teaching, and the values and goals formed from personal history and philosophy of life shaping all three major themes. This case study of creative teaching possesses characteristics resembling creative acts in other domains (e.g., art, literature, physics, economics) and presents a model for the education of future teachers.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".