Women’s Participation in Politics as Represented in the Novel “In Praise of Hatred”
Bibliographic record
Abstract
In today’s reality, there is a definite gap when it comes to men’s and women’s participation in politics. It can be seen that the society prefers men to lead them, make decisions and solve problems. The society assumes men to have better leadership qualities, but people tend to be sceptical when it comes to women. In Syria, men’s responsibilities as leaders and the ones who make decisions are valued highly by the Syrian society. They believe that men’s power and abilities to lead are more stable, prosperous and secure than women. Among the society, women are considered as subordinates and excluded from negotiations. This matter is highlighted in Syrian literature too, especially in novels and writings since masculinity, is practiced in Syrian society. This present study attempted to investigate the gender stereotypes on politics portrayed in the novel “In Praise of Hatred”, by Khaled Khalifa. The present study employed a Critical Discourse Analysis (CDA) approach to investigate the pragmatic representation of politics portrayed in the controversial Syrian novel. The findings focused on the representation of women in politics. To this end, Van Dijk’s Social-political Discourse Analysis Approach was adopted to reveal the ideology behind the constructions. The issues of gender and politics were analysed based on the pragmatic representation in the novel. Adopting the Social-political Discourse Analysis approach under Sociocognitive Discourse Studies (SCDS), the criteria of social aspects (politics and gender) were being looked at thoroughly. Regarding subject positions, the data analysis showed that the portrayal of gender is always biased and women’s participation in politics is not encouraged.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".