Critical arts-based research and knowledge translation: impacts of artificial-intelligence on equality
Bibliographic record
Abstract
This theoretical paper explores the role of critical visual literacy in arts-based research methodologies and knowledge translation, emphasizing relevance in addressing issues of equity and societal impact. Arts-based engagements—including those intended to empower communities—emerge as potentially bringing inadvertent risks given existing evidence base on critical visual literacy. A review of literature identifies an operational definition of principles related to (1) safeguarding marginalized groups; (2) acknowledging diverse interpretation amidst dominant narratives and rule-making by mass-producers of visual media; and (3) analyzing political or other social norms embedded within imagery to prioritize community consent. In response, AI image-generation may support the fostering and application of critical visual literacy in academic settings if the digital divide and historic dataset of visual grammar can be addressed. Under the context of critical visual literacy and participatory engagement, a preliminary framework of machine learning is conceptualized. Integration may offer significant shifts in visual content creation towards critique, with increased capacity for larger-scale production potentially offering opportunities for disseminating new, community-based perspectives on visual grammar. Implications related to co-revision and dignifying the emotional attachment towards art and its critical evaluation conclude the paper.
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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 | 0.060 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.106 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".