Bibliographic record
Abstract
This paper draws attention to links between aesthetic engagement and pre- and in-service teacher self- identity. Generally, my students have little or no background in art or art education. The courses, both graduate and undergraduate, are electives. The paper is divided into two parts. Part 1 begins with an overview of terms and concepts associated with aesthetics and argues for attention to ‘engagement’ rather than ‘experience’. Further, it argues for inclusion of classes in aesthetic engagement throughout public school education as preparation for, and participation in, what Maxine Greene has called ‘wide-awakeness’. That state of being involves an understanding of the place of imagination, empathy, and value discernment in education, and teachers’ moral duty. Part 2 introduces two strategies for prompting engagement with artworks through evocative writing, a ‘show’ versus ‘tell’ emphasis. The writing has a pedagogical goal: to assist in the sharing of experiences so that the teacher can guide students to enlarged aesthetic encounters. But teachers must also engage in writing their own evocative responses, to gain some grasp of the possible range of aesthetic responses and thus achieve credibility in their teaching of aesthetic engagement. Thus, the examples are meant as guides to students and teachers alike. The paper concludes with an example of ‘found’ poetry regarding a particular artwork, followed by the author’s poem devoted to the same artwork.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".