Egyptian Revolution of 2011 and the Power of Its Slogans: A Critical Discourse Analysis Study
Bibliographic record
Abstract
Egypt, the most populated country in the Arab world, erupted in mass protests in January 2011 against the oppressive rule of President Hosni Mubarak. Protesters all over Egypt in general and in Tahrir Square in Cairo wanted Mubarak to leave. Protesters used different dialects, languages, and modes to get their message across. After 18 days of angry protests and after losing the support of the military and the US, Mubarak finally understood the message and resigned on Feb. 11, ending almost 30 years of dictatorial rule. This article builds on studies in Critical Discourse Analysis (CDA) and its implementation of interdisciplinarity to investigate the slogans―fixed expressions, usually chosen carefully by organizers and activists, which are often chanted by political groups and protestors at demonstrations that were used during the Egyptian revolution in late January and February 2011. Moreover, the article shows how CDA―through embracing text as a dialogue and site for interaction, social goods and social languages, interpersonal relations and discourse, multimodality, and intertextuality can help to produce theoretically sound interpretation that is appropriate for the analysis of how Egyptians used the power of language through these slogans to empower themselves, challenge their government, and overthrow the former president Hosni Mubarak.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".