Bibliographic record
Abstract
eaders of this issue of the Canadian Review of Art Education (CRAE) will find five research articles and a large Salon section dedicated to the works of Canadian artists.The first two articles take up art education research in the museum context.Jacob Le Gallais examines the complexity of his childhood memory of museum taxidermy by exploring the nuanced ways art making might confront the seemingly mundane naturalistic display of animals.This work challenges us to critically examine broader discourses surrounding colonialism and the fetishization of nature through an analysis of museological practices, the display of animal bodies, and critical acts of making.Le Gallais' panels Crane Collage 1 [left panel] and Crane Collage 2 [right panel] compose this edition's cover image, and readers are invited to read his discussion of this work in the Artist's Statement.Emma June Huebner examined the use of social media, particularly Instagram and IGTV, as instrumental components in how museum educators engaged virtual visitors during the Covid 19 Pandemic.Huebner looked at how museums used technology, including Instagram and IGTV, to connect viewers to the gallery shows through the materiality of art making demonstrations and lessons posted by the museums.She examined the implications of the loss of material space and the use of technology in broader discourses of gendered work in museological practices.The last three articles explore facets of art teaching practices for their impact on student learning in and through the arts.Barbara Hirst provides a generative discussion of the relational and subjective experience of time through Heidegger's philosophy of time perception.She connects these understandings to how students with ADHD experience time to offer insights into art instruction informed by students' temporal perceptions and the phenomenology of art experience.Tiina Kukkonen and Benjamin Bolden draw on Teresa Amabile's Componential Theory of Creativity for its potential in nurturing existing teaching practices to further support creative development in the art classroom.Marie-Pierre Labrie explores the generative force of a dialogue between design-based research and research-creation in art education.She argues the intricacy of the creative process occurring in art-making may contribute to the methodological endeavours of design-based research.Starting with a theoretical standpoint, the author seeks the two methodologies' epistemological and procedural meeting points.The Salon section features the artists and works of the CSEA/SCEA 2021 virtual exhibit Navigating and Creating, curated by Dr.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.070 | 0.003 |
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; both teacher heads agree on what is shown here.
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".