10 Misogynistic+ Hate+ Speech+against+ Female+ Politicians+on+ New+ Media+in+ Pakistan
Notice bibliographique
Résumé
Abstract Pakistan has been observed considerable rise in women’s political representation similar to other nations, but along with this growing rate of misogynistic attacks have also been noticed. Under the umbrella of anonymity, social values of society constantly dishonoured, prominent political names Maryam Nawaz, Uzma Bukhari, Shiren Mazari, Hina Rabani Khar, Sherry Rehman, and others have faced hate speech, digital abuse that is irrelevant to their political performances. Such moral policing reflects unease of society to see women in leadership roles and holding authority in their hands. New media platforms have important and central space for political discourse in Pakistan’s changing digital landscape. While on one side digital advancement paving the way for global political communication, on other hand technology has led to increase digital abuse and hate speech particularly targeting women in online space. Digital spaces like YouTube, Twitter/ X, Instagram, tiktok, Facebook, and many other platforms become known as powerful tools of technology, at present all traditional media shifted to digital platforms according to the requirement of time. This study “Misogynistic hate speech against female politicians on new media in Pakistan” examines how online misogyny affects its interconnected aspects including perceived political authenticity, political affiliation, political participation, and public shaming. Four hypotheses were created to check the relationship of misogynistic behaviour with interconnected features. The study is descriptive nature study, quantitative research design used for data collection and survey questionnaire disseminated among 220 female political representatives who served at district, divisional, provincial and national level through convenient sampling technique. 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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».