Proposing Favorite Strategies to Produce Social and Political Documentaries for IRIB (Islamic Republic of Iran Broadcasting)—By Purpose of Promoting State Social Capital for Adult and University Students
Bibliographic record
Abstract
Informing and providing awareness is a part of mass media’s significant responsibilities and social and political documentaries is one of the effective frames in performing this significant duty on adults and university students. Therefore, the main purpose of this study is proposing favorite strategies for IRIB in making social and political TV documentaries with the aim of promoting state social capital for university students and adults. The method is in-depth interview with experts and utilizing the findings of another research titled “an analysis of the views of Communication Sciences students regarding the IRIB’s political and documentaries with an emphasis on the utilitarian approach and gratification level” adopting the Rosengren approach. Results showed that aggressive strategies are the most proper policies for the IRIB. Finally, the formulated aggressive strategies were prioritized and the most important strategy for this research is “producing social and political documentaries with informational approach in an accurate, precise, and comprehensive manner and considering other needs of audiences such as amusement, personal identity, and personal relationships”.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".