Finding a voice in 21th century popular culture: Rupi Kaur and her insta-poetry
Bibliographic record
Abstract
Throughout history, women who have been oppressed by male hegemony and had to face inequality in many areas of life because of their gender, have fought for their freedom and gender equality by rebelling with the feminist movement that started in England in 1792. Mainly serving a common purpose, the feminist movement was separated by four waves. Women, who won political rights with the first wave, criticized the patriarchal structures and cultural norms at all levels of the society in the second wave, and fought for the destruction of gender roles and the elimination of any gender superiority. Aware of the racist, sexist and classist barriers created by the patriarchal order, the third wave feminists refused to be a part of this system and defended the rights of women of different races, colours, classes, religions, cultures and ethnic origins by focusing on their problems. Their ideas on different femininity issues reached wider audiences thanks to the technological opportunities brought by the 20th century such as electronic magazines and blogs. The fourth wave of feminism, which started in 2012 and is taking shape today, draws attention to the problems of sexual harassment, rape, domestic violence, sexism and misogyny via social media which has a significant impact on the formation and spread of popular culture. Thus, all over the world, women from different ethnicities who have been subjected to violence or sexual harassment have made their voices heard by sharing their horrible experiences on social platforms. These traumas experienced by women in addition to the aesthetic concerns brought by the 21st century have alienated them from their own bodies and made them lose their sense of belonging. The Indian-Canadian poet Rupi Kaur, finding a voice within popular culture, draws attention to different experiences and problems of womanhood with her instapoems which she first shared on Instagram and Tumblr and later published in her books Milk and Honey (2014) and The Sun and Her Flowers (2017). Kaur has succeeded in being the voice of all women by boldly expressing the issues that are covered up in society such as sexual harassment, rape, cultural structures regarding female bodies, taboos and the problems faced by non-white women through her digital feminist activism. While doing this, Kaur, who deals with the problems expressed by women in the third wave through the identities of her own mother and the other women in her community, combines her own experiences with the premises put forward by the digital feminism as part of the fourth wave. Thus, she not only creates a bridge between 20th and 21st century feminisms, but also reveals the path taken in terms of feminism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".