MENA Region Transformed Media Environment and Media Convergence: UAE Case Study
Bibliographic record
Abstract
It is worth noting that the concept of media convergence entails the process of multiple media platforms coming together to combine their equipment and tools towards enhancing the production and distribution of news or information (Dwyer, 2010). In essence, it demonstrates the collaboration or cooperation between diverse media platforms to create a blend of computer, telecommunications, and media industries that can eliminate the barriers to media unity (Lugmayr & Dal, 2016). It embraces the integration of various media forms into a single digital platform. The diversity and dynamic experience encountered through the process of media convergence enables the professionals in the sector to elaborately communicate information and tell stories (Latzer, n.d.). They can offer entertainment to the audience in a convenient manner while enhancing the interactivity of the media platforms for the unique experience. Ideally, Jin (2011) explains that the concept of media convergence is related to the aspect of technology convergence in the sense that it entails the combination of diverse technologies used in the media system in conveying information. The technology convergence facilitates the media content production through the expansion, acceleration, and enhancing its distribution with the reduction in costs (Jacobs, 2013). According to the Australian Law Reform Commission (n.d.), the technology convergence relies on the diversity of available media devices or gadgets that are utilized in the transmission of information and data. As such, the application of technology convergence enhances quick, safe, and convenient mechanisms of passing information to the consumers through the digital platform. Lugmayr and Dal (2016) indicates that the development of the cross-media content enables the media stations and professionals to provide the information in varied modes including videos, texts, audio, print, and the podcasts. Notably, this paper focuses on discussing the concepts of media convergence within the context of the MENA region, specifically in the UAE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".