Role of Media in Representation of Sociocultural Ideologies in Aurat March (2019–2020): A Multimodal Discourse Analysis
Bibliographic record
Abstract
The researchers have explored the role of print media and social media to present the social, cultural and political ideologies through the support of liberal feminist women in Aurat March 2019–2020. Moreover, the researchers have identified the connection/s between the language and power in the construction of ideologies, specifically through the media (print and social media). Print media, specifically print social media, has a negative impact due to its lesser amount of validity and a positive keeping wide coverage. For this study, researchers took three articles from three different local newspapers about the specific topic “Aurat March”. These articles along with the posters (which were present in the specific articles) of Aurat March have been analyzed. The researchers collected the data through a qualitative approach and purposive sampling. The research is exploratory and multi-directional. Fairclough’s model of critical discourse analysis is used for the analysis. The findings of the study have suggested that media discourse is intentionally crafted to create specific ideologies. As media has created and represented different socio-cultural ideologies in Aurat March. Media can play a positive and negative role in language and power. However, the impact of the media’s ideologies is depending on the feedback of the concerned society.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".