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How I love and hate my idol: Derisive fandom (Ruzhui) and the tug zone of identity, emotion and morality in Chinese fan culture
Wenjun Zhao, Pengfei Fu
European Journal of Cultural Studies·2026
This study investigates derisive fandom (
Ruzhui
), an ambivalent practice within Chinese fan culture, in which fans simultaneously deride idols while remaining deeply invested in them. Based on 1 year of participant observation and 28 in-depth interviews, this study explores how these derisive fans negotiate their identities, emotional attachments and moral standpoints in the context of a highly commercialized fan economy and the moral governance of Chinese society. Challenging the dominant binary between fan and anti-fan, this study conceptualizes
Ruzhui
as occupying a ‘
tug zone
’, a suspended affective space where conflicting attachments coexist and resist resolution. Rather than representing a departure from fandom, derisive practices reveal how critique and devotion can emerge from the same emotional investment. By foregrounding this affective ambivalence, the article contributes a new perspective to anti-fan studies, highlighting how contemporary fan identities are shaped not through stable categories but through dynamic negotiations of emotion, consumption and normative regulation.
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View paper →Psychotherapy is Not Moral Education
Jonathan Shedler
Substack·2026
A psychotherapist does not know how you should live your life.
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View paper →Namwali Serpell on Approaching Toni Morrison’s Work As a Reader and a Critic
Jane Ciabattari
Literary Hub·2026
No abstract available
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View paper →how white fear killed the romantic comedy.
ayan artan
Substack·2026
on the narcissism of the modern romance & the dishonesty of nostalgic viewing.
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View paper →Symmetry in language statistics shapes the geometry of model representations
Dhruva Karkada, Daniel J. Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri
arXiv·2026
Although learned representations underlie neural networks' success, their fundamental properties remain poorly understood. A striking example is the emergence of simple geometric structures in LLM representations: for example, calendar months organize into a circle, years form a smooth one-dimensional manifold, and cities' latitudes and longitudes can be decoded by a linear probe. We show that the statistics of language exhibit a translation symmetry -- e.g., the co-occurrence probability of two months depends only on the time interval between them -- and we prove that the latter governs the aforementioned geometric structures in high-dimensional word embedding models. Moreover, we find that these structures persist even when the co-occurrence statistics are strongly perturbed (for example, by removing all sentences in which two months appear together) and at moderate embedding dimension. We show that this robustness naturally emerges if the co-occurrence statistics are collectively controlled by an underlying continuous latent variable. We empirically validate this theoretical framework in word embedding models, text embedding models, and large language models.
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