Critical arts-based research and knowledge translation: impacts of artificial-intelligence on equality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it