Self-Reflective, Contextual, Multi-Modal Auto-Ethnographic Work: An Approach to Teaching Visual Art Inquiry
Bibliographic record
Abstract
Visual auto-ethnography offers a way to engage students in self-directed arts-based inquiries focusing on material culture constructs (Garber, 2019). It facilitates creative work and meaningful connections to visual culture (Bolin & Blandy, 2018; Freedman, 2003). Situated in Bourdieusian (1984) habitus lenses, Pink’s (2013) visual ethnography, and drawing on material and visual culture sources in students’ lives, ideas and making practices are explored and expressed. As a multi-modal student-centered pedagogical approach, drawing on the idea of a cabinet of curiosities (Mauries, 2011), and Adam’s (2015) non-linear process, autoethnography involves orienting to a personally relevant inquiry question, examining personally curated art and/or material culture artefacts, engaging in focused journaling, gathering relevant multi-modal artifacts and visual art as copies or originals, and finally, creating multi-modal artworks and accompanying texts that inform and complement one another. A student might examine, for example, lived experiences of spaces and places, situated alongside aesthetic experiences of spaces and places in visual art and culture. Contextual, multimodal and personally relevant, this approach is suitable for pedagogical settings ranging from middle school to higher education via arts education courses, advancing possibilities for situated focused creative work through reflective praxis. The work of Calle (2003) is helpful in locating forms of documentary work in contemporary art, and the work of Eldridge (2012) locates autoethnography in art education scholarship. It is clear that auto-ethnographers’ voices and visions represent unique, situated worldviews, facilitating individual and collective understandings and respect for diverse cultures and perspectives.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".