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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.024 | 0.062 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".