Ar(c)tivism and Policing: Unveiling the Theatrics of Justice and Resistance in Nigeria’s Sọrọ-Sókè Movement
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
The Sọ̀rọ̀-Sókè movement, sparked by Nigeria’s 2020 #EndSARS protests, represents a pivotal stand against systemic injustice, with its Yoruba rallying cry “Sọ̀rọ̀-sókè” (“Speak Up” or “Speak Louder”) capturing the collective demand to end police brutality, notably, by the Special Anti-Robbery Squad (SARS). This study employs Digital Artivism as its theoretical lens to investigate the fusion of art and activism within the movement, analyzing how creative and performative expressions amplified its message and mobilized diverse populations. Applying Feldman’s Model of Art Criticism, it dissects the theatrical elements of selected protest artworks, revealing their role in inciting resistance and fostering solidarity in the pursuit of justice. By situating Sọ̀rọ̀-Sókè within global discourses on art and social justice, this research underscores its significance as a model of artivism’s power to challenge oppressive systems and inspire collective action. The critique of these artworks illustrates their lasting influence on Nigeria’s socio-political landscape and their resonance with worldwide struggles against systemic violence and inequality. Highlighting the transformative potential of theatrical activism, this study advances understanding of how digital artivism can unite voices, elevate causes, and drive societal change.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it