Ethical Dilemmas in Resistance Art Workshops with Youth
Bibliographic record
Abstract
In 2017 and 2018 [Name of research project] organized two transnational youth resistance art workshops. These workshops addressed online social justice issues and placed emphasis on pushing back against technology-facilitated violence and surveillance in networked spaces. Our engagement with these workshops raised three dilemmas associated with these sorts of resistive social justice art projects. This article explores these dilemmas, which include how to enable the production of digital art in a manner that is attentive to intersectional issues of digital literacy and access; artistic appropriations of sexually explicit, discriminatory or hateful speech and their relation to cultural appropriation; and defamation, privacy, copyright and trademark considerations relating to artistic appropriations. In addressing these dilemmas, examples of regulatory frameworks shaping resistance opportunities and social justice initiatives are highlighted, along with suggestions for addressing these dilemmas for those who may wish to facilitate or engage in youth resistance art workshops in future.
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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 | 0.065 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.030 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".