10 Misogynistic+ Hate+ Speech+against+ Female+ Politicians+on+ New+ Media+in+ Pakistan
Bibliographic record
Abstract
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. This research study aims to analyse and understand the perception of female politicians because these are the one who directly encountered misogyny on new media platforms. This study emphasizes how misogyny structured both public narratives and victim’s attitude through digital spaces. References Anees, M. (2022). Cybercrime law in Pakistan: An ultimate guide . Graana. https://www.graana.com/blog/cybercrime-law-in-pakistan/ Anees, S. M. (2023, May 24). Where are the women in Pakistan's politics? The Diplomat . https://thediplomat.com/2023/05/where-are-the-women-in-pakistans-politics/ Anderson, M. (2016, August 15). The hashtag #BlackLivesMatter emerges: Social activism on Twitter. Pew Research Center . https://www.pewresearch.org/internet/2016/08/15/the-hashtag-blacklivesmatter-emerges-social-activism-on-twitter/ Azeem, M. (2020). Islam, Pakistan and women leadership: A case study of Benazir Bhutto. 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BBC News . https://www.bbc.com/news/world-us-canada-50563106 Sakki, I. (2023). Emotion, language and communication, race, ethnicity and culture: Contested meanings and uses of hate speech. The Psychologist, 36 , 32-35. https://www.bps.org.uk/psychologist/contested-meanings-and-uses-hate-speech Shehab, R. (1989). History of Pakistan . Sang-e-Meel Publications. Spark, S. L., & Tuysuz, G. (2014, November 25). Rights groups slam Turkey's Erdogan over remarks on women. CNN . https://edition.cnn.com/2014/11/25/world/europe/turkey-erdogan-women/index.html Tribune. (2023, September 10). Cyber crime in Punjab increased manifold in past quinquennial. https://tribune.com.pk/story/2445839/poor-policing-cyber-crime-in-punjab-increased-manifold-in-past-quinquennial United Nations. (2021, March 16). Female government leaders recount personal experiences of threats, attacks, structural obstacles [Press release]. https://press.un.org/en/2021/wom2206.doc.htm United Nations Population Fund. (2022). 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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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; both teacher heads agree on what is shown here.
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".