Development Optimal Strategies for Media Policy of IRIB on Issue of Climate Change
Bibliographic record
Abstract
The main objective of this research is developing optimal strategy for IRIB in relation to climate change. Arising Threats from climate change for human life and other creatures resulted in different media productions, all around the world. It is needed for IRIB to pay more attention to climate change problem in its programs, far more than current talk shows with experts. Achieving this goal with no strategy seems impossible. In order to develop an optimal strategy for IRIB in relation to climate change, with the use of "in-depth interview" method, researcher provided the needed information from a couple of interviews with environment experts. With the use of in depth interviews, at first, current situation of climate change topic in IRIB, and then the responsibility of IRIB in relation to climate change were discussed. The next step was finding required information about climate change for a typical audience; this information is extracted from interviews with climatology and environment experts. Using internal and external factors' analysis on IRIB performance on climate change, IRIB strengths, and weaknesses on subject and external threats and opportunities for IRIB in climate change topic were specified, and by the use of SWOT analysis all possible strategies were found. Finally the researcher found that optimal strategy for IRIB is an aggressive strategy.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".