The Influence of Turkish Drama on Socio-Cultural Values of Saudi Women
Bibliographic record
Abstract
Dramas made in Turkey are the most popular soap opera-type in the Arab world and are often watched by women. This study aimed to investigate, from a critical perspective, the impact that Turkish dramas (TDs) might have on developing attitudes of Saudi women. While more than 60 country-specific studies in the Middle East and North Africa have investigated the impact of dramas on audiences, Research regarding Saudi Arabia is surprisingly lacking. Therefore, the effect of TDs on Saudi women was examined as part of this study. Qualitative methodology was used, and semi-structured interviews were conducted with eight Saudi women (aged between 18 and 40) who have watched TDs. Following the groupings, qualitative analysis techniques were employed as part of thematic analysis (TA). The responses of Saudi women highlighted the different types of impacts that TDs have on Saudi audiences: positive, negative, and neutral. The study made a small but essential contribution to an under-researched issue of pleasures of TDs and their representational heuristics in capturing both positive, negative, and so much in between for a particular cohort of Saudi women. It is the first qualitative examination of the impact of TDs on Saudi women's attitudes.
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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".