The digital transformation of Arab news: Is there a future for online news after the ‘Arab Spring’?
Bibliographic record
Abstract
This study examines online news delivery and how Arab newspaper journalism has harnessed the Internet’s potential to deliver news in novel ways, reach new readers and audiences, and the implications for the way these users consume and interact with online news. Existing research on online journalism has documented the web’s effects on journalism practice and online news at several levels, including the reorganization of newsrooms and the incorporation of technical features such as interactivity and media convergence. Based on those research findings, we conduct a comparative analysis of 54 websites of online and print newspapers in the Arab world. The comparative analysis focuses on six main variables: (1) revenue resources, (2) editorial organization, (3) hyperlinks, (4) interactivity, (5) media convergence issues, and (6) updating/immediacy. While the Internet has opened new and immense opportunities for journalism in the region, the study finds little evidence to suggest that it is substantially contributing to transforming communication dynamics and journalistic practices that can foster dialogical discourse and participatory communication, both of which are central to the development of civic culture and liberal democracy.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".