Ethical Sustainability in Scene Design of Iranian Children TV Shows
Bibliographic record
Abstract
As the most efficient cognition source of children, TV programs can play an important role in fostering the priorities of Ethical Sustainability. Lack of attention in the field of scene design is addressing the absence of ethical sustainability requirements in the scene design principles. But, do the scene design principles in Iran have shortcomings? The aim of this paper is to define these shortcomings on the basis of ethical sustainability and present recommendations which can be considered by Iranian scene designers. The adopted methodology of research is case study, answering the research questions via qualitative data collection and descriptive-analytical technique. To achieve the aim of the research, the importance of ethical sustainability in the scene design of children TV shows is highlighted and the effect of design principles established by Islamic Republic of Iran Broadcasting Organization are reviewed through relevant literature and reliable documents. Then, through a comparison between Sustainable Principles of Hannover and the mentioned Iranian design principles on the basis of sustainable relations, shortcomings of principles are discovered. In addition, the selected case study of the research is the scene of “Fitileh TV show” which was successfully designed in accordance to the mentioned Iranian design principles. The current practice of established design principles are examined through direct observation and visual evaluation and presented in a clear view. The effects of the shortcomings of the afore-mentioned design principles on the designed scene are evaluated afterwards and the recommendations are presented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".