The <i>Arab Street</i> : Tracking a Political Metaphor
Bibliographic record
Abstract
Understanding Arab public opinion is central to the search for sustainable political solutions in the Middle East.The way Westerners think about Arab public opinion may be affected by how it is referred to in their news media.Here, we show that Arab public opinion is rarely referred to as such in the US media.Instead, it is usually referred to as the Arab street, a metaphor that casts Arab public opinion as irrational and volatile.We trace the origins of this metaphor to similar expressions in both English and Arabic, and note similarities and important differences between the English and Arabic usages.Ultimately, we argue that the Arab street metaphor misrepresents the Arab public, and invites dismissal of rather than engagement with Arab public opinion.Arab public opinion helps shape the modern Middle East, whether through the ballot box, support for armed resistance, or otherwise.Regardless of one's political stance, Arab public opinion is inescapably important, and one would be unwise to dismiss or mischaracterize it.Yet current usage in the US media invites readers to do just that by regularly referring to Arab public opinion metaphorically as the Arab street.we will argue that this metaphor constructs Arab public opinion in a stereotypical, inaccurate, and pejorative fashion.Thus, the widespread use of this metaphor has the potential to obscure the actual nature of public opinion in the Arab world, 1 and to impede engagement with it.This metaphor has received some analytical attention, 2 but despite its centrality in the US media, to our knowledge no comprehensive study of its use and origins has yet been conducted.Here, we present the results of such a study, based primarily on the archives of English and Arabic newspapers over the past several decades.we sought to answer three questions:1. Is the Arab street an innocuous metaphor for Arab public opinion, or does it invite a negatively framed and inaccurate stance toward that public?2. Is this metaphor used frequently enough that it might have an appreciable effect
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".