Study of Orientation and News Coverage about Daesh Crisis in Websites of IRNA, BBC and Voice of America
Bibliographic record
Abstract
This paper, entitled “Study of orientation and news coverage about Daesh crisis by websites of IRNA, BBC Persian and Voice of America”, comparatively explores structure and how to cover news in websites of IRNA, BBC Persian and the voice of America. The paper is conducted in descriptive-analytical method and by the way of content analysis. Data has been studied in library method. Survey and examination of existing documents as well as analysis of the content of news websites are presented in descriptive tables. The type of organization and how to cover news differ in three websites of BBC and IRNA, Voice of America. Every three websites have specifically emphasized on the element “who’’, “news value’’ and “fame’’, indicating their person-oriented and talk orientation. Finally, it was concluded that most published news are “non-productive’’ in three websites, however IRNA’s portion is more than others. Conceptually, the issue of Daesh group resistance, the employment of Jihadi groups, the effort to manifest Daesh an Islamic group, introducing Daesh governmental philosophy as an Islamic government and not mentionning to its terrorist nature has been under survey and attention of these three websites. The focus of IRNA and Voice of America is mostly on “hard news’’ but BBC concentrates on “soft news’’ or reflection of events along with complementary information. Publication of “photo’’ has been used as interactive and multi-media facilities by news websites. Unlike IRNA, websites of BBC and Voice of America have highly made use of Email and links to topics and relevant websites.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".