Negotiating the Location of Art Education
Bibliographic record
Abstract
This article seeks to articulate developing trends in art education and practice, locating such movements within the broader cultural contexts of globalization, neoliberal capitalism, and postmodernity. Against this more general synopsis, the autobiographical position of the author as a student and teacher of art will be elucidated as inextricably entwined with such cultural movements. This entwinement will be understood both in terms of its capacity to ‘position ’ the subject, and yet concomitantly as a site of disavowal, refusal, and subjective agency. In this manner, the personal commitment of the author to art education will be developed in a way to implicate early school and familial experiences with art. Such early autobiographical experiences arguably form the coordinates of our identities as art educators, and similarly, constitute the key issues with which we must necessarily grapple in pedagogical practice. It is in negotiation with such issues and early enculturation that this article argues our relationship to art curriculum and practice is located. Preamble Revised in 1985, the Alberta Art curriculum emerged in a turbulent time punctuated by the ultra conservative tone of American Reaganomics and the lingering ideological, economic and geopolitical anxieties of the Cold War. As the atmosphere of many schools veered toward competitiveness and standardized achievement as a measure of ranking ‘cultural capital ’ on an international stage, many national art education
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".