The Politics of Arab Pop: Arab Pop Music, Popular Culture, and Gender Norms in Lebanon
Bibliographic record
Abstract
On October 17, 2019, thousands of Lebanese flooded the streets to protest governmental corruption.Soon after, issues of gender inequality were added to the list of grievances and women began leading marches to oppose the socio-political and economic burdens faced by women.Rage quickly consumed the whole of Lebanon, including -perhaps surprisingly -many of the country's biggest pop music stars.They marched in the streets, sung nationalistic songs, and participated in the feminized re-writing of the national anthem.Using the case of pop music in Lebanon, this dissertation examines how forms of popular culture intersect with gender politics.More specifically, I argue that pop music is deeply connected to politics and that many pop music celebrities actively work to promote changes in (and awareness of) gender and sexuality norms in Lebanon.I also problematize cases where artists' actions actually reinforce and reproduce restrictive gender norms.In making these claims, however, I endeavour to remain mindful of the socio-political realities of the Lebanese context that work to impede efforts to promote and secure gender-based reforms.I am also attentive to my own heritage as a student researcher of Lebanese descent and reflect on how this 'insider' identity has come to shape my work.To guide my analysis, I rely on the insights of (feminist) intersectionality and highlight the need for nuanced understandings of power and agency.Given the seriousness of Lebanon's current political economic crisis, I also discuss how popular music celebrities have been eager participants in the 'Lebanese Revolution' since its outset.Finally, I discuss the COVID-19 pandemic and the Beirut Port explosion, arguing that some pop stars' social media posts work to reproduce harmful gendered discourses, while others worked to give voice to the feelings of frustrated Lebanese desperately seeking accountability from their government.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".