Bibliographic record
Abstract
Abstract The relationship between space, language, and the active construction of political identity in Lebanon is explored through a diachronic look at the outdoor display of political rhetoric of the Lebanese political leader, General Michel Aoun, and his evolving party, the Free Patriotic Movement (FPM), in the public landscape of Beirut. Specifically, I will focus on graffiti of the FPM in 2005 and discuss its resonances with the earlier political rhetoric of Aoun during the civil war and his Paris exile, at a time when he was in a conflicted relationship with the state. I will then look at his later political advertising campaigns from 2008–2009 to see how some semiotic elements from Aoun's time in exile and his return in 2005 have been extended into the more recently professionally polished party image. Through the examination of linguistic strategies, such as the use of reported speech and graffiti font, I argue that these textual artifacts contain the residue of contested ideologies of populism, nationhood, and belonging, which gain particular meaning when placed in specific geo‐semiotic zones within the political landscape of the city. These ideologies formed the basis for constructing a graphic identity for Aoun in subsequent advertising campaigns. The publicly placed written word, then, provides a medium through which political identity is formed, negotiated, and revised. [political discourse, Lebanon, arabic, advertising, graffiti]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".