Arab Revolutions: Breaking Fear| #Hashtags for Change: Can Twitter Generate Social Progress in Saudi Arabia
Bibliographic record
Abstract
Since the Arab Spring uprisings in 2011, Twitter has proven to be a useful mobilization tool for citizens. The power of Twitter to mobilize citizens (as seen in the Arab Spring) worries some governments. In response, a number of countries have begun to censor access to Internet technology. The Saudi monarchy, for example, issued a decree banning the reporting of news that contradicts sharia (Islamic) law, undermines national security, promotes foreign interests, or slanders religious leaders. A key question requiring further examination is why the Saudi government issued this decree. Are these controls in place to manage the Kingdomof Saudi Arabia’s political image on a global level, or are they in place to regulate the morality of its citizens at the local level? Drawing upon the work of Manuel Castells and his discussion of network power, this article asks: Can Twitter usage promote social progress in Saudi Arabia?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".