Defining Artful Literacies: Adolescent Affects, Belonging and Cross-Sectorial Creativity
Bibliographic record
Abstract
Museum education is tightly related to arts-based education, an area typically associated with the intersections of such disciplinary domains as the arts, museum research, digital humanities, and literacy studies. A closer look at these intersections paves the way for emergent research investigating what the concept of the museum does and means for nondominant (BIPOC, 2SLGBTQ+) adolescents aged 12-19 enrolled in accessible youth programs who mobilise ‘artful literacies,’ a term we adopt and define in this chapter. To define artful literacies and subsequently interrogate the concept of the museum as a “thing” (Barad, 2007), we look across data from a CLARI-funded project in the Maritimes to propose a dynamic, and emergent re/definition of museum education in which adolescents voice and articulate their perspectives on their sense of belonging in museum spaces. In particular, non-linear data from semi-structured interviews nurtures dynamic meanings of beingwith adolescents' perspectives on museums, which inform the very concept of artful literacies. Finally, in defining artful literacies, this chapter points to significant contributions to conceptualizing the idea of the museum as a rhizomatic space that differs from the traditional, artwork-curated museum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".