Bibliographic record
Abstract
Abstract This chapter invites critical scrutiny of the role of performance ethnography in development praxis, focusing specifically on the place of ethnomusicology in current discourses about alternative frameworks for transitional justice in post-conflict and fragile states. The paper responds to the increasing appeal in transitional justice literature for legal pluralism and reflects on the challenges and opportunities that traditional justice strategies pose for many of the fundamental assumptions that currently underlie post-conflict rule-of-law work. Taking direction from Brown et al. (2011) and Mignolo (2013), who call for imaginative “delinking” from current epistemic hegemonies in seeking solutions to pressing societal problems, the chapter argues for greater consideration of culture in responding to the multidimensional legacies of protracted conflict (Rush & Simić 2014). Drawing on research on Dinka ox-songs in South Sudan—a country that emerged from half a century of civil war with Sudan, but remains profoundly destabilized by internecine violence—the paper argues that in their capacity as public hearings, ox-songs offer locally embedded judicial instruments or “justice rituals” (Rossner 2013) of narration, listening, and understanding, opening discursive spaces for the expression of multiple public positions and forms of agency. While songs recount individual, clan, or community memories within the context of culturally legitimate expressive spaces, they equally reveal potentially incompatible rejoinders to social justice, forgiveness, and inclusivity, thus supporting new pathways for hybrid or plural frameworks for truth-telling, justice, and reparative outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".