Visualizing art education in the twenty-first century: Mapping the themes of art educators through the NAEA convention, c. 2000–2015
Bibliographic record
Abstract
Abstract This article presents a visual analysis of the National Art Education Association annual conference programmes from 2000 to 2015 to provide an understanding of the themes and topics that practitioners in the field of art education have presented in the twenty-first century. Over this period, themes such as curriculum, learning and teaching were consistently represented, while themes such as aesthetics were less used and themes such as visual culture emerged. Given the advancement of digital data visualization methods, we revisit the convention catalogue as a rich source of archival material to identify the thematic patterns and diversity in our field. Data visualizations can assist in making visible certain patterns and trends that can confirm, run counter, or diverge from our individual perception of an event. In this article, we identify some of the persistent, fading and emergent themes pursued by art education practitioners. We conclude by examining the theme elements and principles, which have considerable importance in the recent literature on art education, yet are curiously absent from the themes in convention presentations.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".