Picturing a Past in School: Situating Culture(s), Histories, Language(s) and Power in Fine Arts Teacher Training
Bibliographic record
Abstract
In this paper, I ask provocatively what it is that we do when we teach about art to students in schools and in university teacher training programs. I attempt to think through the necessary relations that exist in secondary fine arts teacher training. Within a hermeneutic unfolding I consider the difficulty of seeing, speaking, and listening for myself and my students, considering not what one is to teach, nor even how one is to teach, but how omissions and absences inscribe partial, located, ahistorical, and dominant ideological narratives about what and who counts, what constitutes the good, and what knowledge is of most worth. The implications of understanding the past, and its place in our present dealings gives us important insights into imagining our futures and considering how we and our students might live well in teacher education in a changing world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".