Are scholars of magic trying to tell practitioners what to believe, or is this a misunderstanding of what academic research actually does?

In this video, I explore the tension between the academic study of magic and lived spiritual practice. Drawing on debates around Wicca, Pagan origins, folk magic, historical evidence, and practitioner identity, I explain why scholarship can challenge historical claims without invalidating religious experience.

This is a nuanced look at authenticity, tradition, myth, evidence, and why a practice worth keeping has nothing to fear from the truth.

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Is Litha really an ancient Pagan festival, or is its history far more fascinating?

In this episode, Dr Angela Puca explores the true history of Litha, the Summer Solstice, and Midsummer, separating historical evidence from modern Pagan myth-making. We look at Stonehenge, Bede, St John’s Eve, midsummer bonfires, folk magic, Wicca, and the creation of the modern Wheel of the Year.

Rather than asking whether Litha is “real” or “fake,” this video reveals how contemporary Pagans transform ancient solar symbolism, European folklore, and modern ritual creativity into a meaningful seasonal celebration.

Perfect for Pagans, witches, Wiccans, folklore lovers, and anyone interested in the history of magic, ritual, and the Summer Solstice.

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MY COURSES 👩🏻‍🎓 https://drangelapuca.com/courses

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RECOMMENDED READINGS 📖

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(Check the related slide on screen)

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I keep being accused of using AI. I've even been accused, more than once, of being AI-generated. So I owe you something better than irritation: an actual explanation of where I stand.

In this episode, I work through the real concerns: the scraping of artists' work, the environmental cost, algorithmic bias, the fear of job displacement, the worry about deskilling, and argue that every one of them is a problem of how, not of whether. They are arguments for regulation, not for personal abstention. I talk about my own practice (yes, AI images sometimes; yes, Grammarly; no, not the writing or the thinking), about teaching at university in the middle of all this, and about why, as an anthropologist, I think this debate is really a debate about authorship and authenticity wearing a technological costume.

The question, in the end, was never if AI. It was always, only, how.

CONNECT & SUPPORT💖

MY COURSES 👩🏻‍🎓 https://drangelapuca.com/courses

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BOOK A TUTORING OR A LECTURE 📖https://drangelapuca.com/services

SUBSTACK ✉️ https://substack.com/@drangelapuca

BECOME MY PATRON! 🎩 https://www.patreon.com/angelapuca

SUPPORT ME ON KO-FI ☕️https://ko-fi.com/drangelapuca

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REFERENCES 📚

Cognitive offloading and deskilling

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Training data, consent, and copyright

Buick, A. (2025). Copyright and AI training data – transparency to the rescue? Journal of Intellectual Property Law & Practice, 20(3), 182–192. https://doi.org/10.1093/jiplp/jpae102

Dornis, T. W., & Stober, S. (2025). Generative AI training and copyright law. Transactions of the International Society for Music Information Retrieval, 8(1). https://doi.org/10.48550/arXiv.2502.15858

Algorithmic bias and discrimination

Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31

Rosenthal-von der Pütten, A. M., & Sach, A. (2024). Michael is better than Mehmet: Exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring. Frontiers in Psychology, 15, 1416504. https://doi.org/10.3389/fpsyg.2024.1416504

Labour displacement and the reshaping of work

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3

Environmental cost of AI

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355

Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). Environmental cost of AI's energy use: Carbon, water and land footprints. United Nations University Institute for Water, Environment and Health. https://doi.org/10.53328/INR26RMA002

Regulatory competition and the US–EU asymmetry

Smuha, N. A. (2021). From a 'race to AI' to a 'race to AI regulation': Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.1898300

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Music by Erose MusicBand. Check them out!