Arab Revolutions: Breaking Fear| The Arab Spring & Online Protests in Iraq
Bibliographic record
Abstract
This article traces the influence of the Arab Spring on Iraq as activists staged fervent protests against corruption, sectarianism, and favoritism that largely characterize Nouri Maliki’s government. A group of young Iraqi intellectuals, journalists, students, government employees, and unemployed youth posted their plan to organize demonstrations against the government with the use of social media in February 2011. This study investigates the use of Facebook and YouTube that bypassed the government’s attempt to limit the coverage of these protests. Indeed, the events during the Arab Spring in Iraq crossed the sectarian lines and united different Iraqis against the Shiite-dominated government. The five most popular Facebook pages are examined together with over 806 YouTube clips that are related to the Iraqi Arab Spring and their 2839 comments. The study revealed that young Iraqi males aged between 25-30 were the most active vloggers, while those aged between 20-24 were the most active commentators during the protests. Further, the study showed that other Iraqis living in the Diaspora especially in the United States and Canada played an important role by posting YouTube clips and comments. Also, there was a great gender disparity since Iraqi male users surpassed females in the number of video clips and comments posted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".