Bibliographic record
Abstract
This paper explores the way in which hip hop artists Iraqi British Lowkey, Iraqi Canadian Narcy, and the Palestinian crew DAM deploy music to challenge narratives of terrorism that are constructed to achieve political objectives and consequently obfuscate geopolitical inequities. Central to these narratives is the figure of the terrorist who is often conflated with Arabs, Muslims, Middle Easterners, and those who express dissent against these narratives. All three artists use hip hop to deconstruct the narratives of terrorism in order to address the perspectives of marginalized groups and to problematize their use. Through their music, Lowkey, Narcy, and DAM contest definitions of terrorism that are differentially applied to certain communities and do not address the use of violence, specifically state violence, to achieve political objectives. These artists suggest an alternative framework where terrorism is not determined by a specific cultural, ethnic, national, or religious affiliation, the root causes of violence are considered, and the complex geopolitical landscape contextualized.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".