Digital Media for Social Justice and Change: Conceptualizing Impacts of Artificial Intelligence on Marginalized Media Creators
Bibliographic record
Abstract
Existing research documents that in a technologically connected society, digital media can often shift population-level ideologies surrounding social justice and social work. Additionally, evidence indicates increased digital-media consumption patterns given how marginalized individuals can face greater barriers in physical participation. Based upon such rationale, this conceptual paper investigates how artificial intelligence serving as digital-media creation tools may impact the lived experience of those who face marginalization due to age, gender, race, Indigenous ancestry, rural geography, disability, and socioeconomic status. This paper reports qualitative data from a parent study engaging with marginalized individuals (n = 8) experiencing 1) intersectional statuses associated with stigma and 2) ongoing barriers to participation in formal learning opportunities, to assess how digital media play critical roles in shaping their access to new information, beliefs, and worldviews. Informed by anti-oppressive and trauma-informed principles in social work, the research employed semi-structured interviews guided by a collaboratively developed framework – CATER (Collection, Action, Transformation, Emotion, Recommendation). Participants reported an average of more than three statuses of marginalization, and were invited to share their lived experiences – specifically informing how marginalization impacts their autonomous creation of digital media and engagements with machine-learning technologies. A sevenpart framework of AI in social-work-oriented digital media creation is thus conceptualized to consider: inclusivity in narrative dissemination; financial barriers intersecting with socioeconomic status; dominant versus counternarratives; market influences; and AI’s critical shortcomings in terms of visibility and audience receptivity. Implications for social justice and social work with marginalized groups conclude this study.
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How this classification was reachedexpand
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".