Bibliographic record
Abstract
The media has assumed a central role in shaping public opinion particularly with the rise in protest activity during the recent uprisings in the region. The influx of news caused an information revolution that played an important role in undermining the state monopoly on knowledge and information. During the Arab Spring, the media transformed and worked to disseminate information faster and to a more diverse range of people. Thus, the media has become more involved in the Arab Spring and expanded its role from previous revolutions. The media began a competition between political actors and changed the manner in which these actors responded to political and ideological conflicts. This research is important because it sheds light on how the media influences Jordanians’ views on the Arab Spring. However, this study was limited because it accounted only for the opinions of Jordanians, and hence does not necessarily reflect the opinions of citizens in other Arab nations. To fully understand the impact of the media on the Arab Spring, we examined the results of a survey done by the Center for Strategic Studies and analyzed them along with other statistics on the rise of social media. Through our analysis, we found that Jordanians believe that the news is biased and has a hidden agenda. This led us to question whether Jordanians were using sources other than traditional media for their information on the protests. From this, we have created the following hypothesis: social media’s role in the Arab Spring as a news agent was largely due to dissatisfaction with the current and conventional news agencies’ manner of reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".