Materializing Magic: How the Witches of Instagram Make the Invisible Visible Through Digital Photography and Editing Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, contemporary witchcraft has become increasingly subject to commodification, with many practitioners criticized for their pursuit of the “witch aesthetic” on social media. With Instagram feeds featuring carefully staged photographs of tools, materials, and spaces related to the craft, these “Insta-witches” or “#witchesofinstagram” often use photo editing applications like Photoshop to digitally manipulate these images in order to enhance their magical or otherworldly dimensions. In this article, I argue that the excessive use of magical “stuff,” along with the staging and editing of images shared on social media does not have to be superficial or devoid of meaning. Rather, I explore how these Witches of Instagram participate in what anthropologist Jennifer Deger refers to as “thick photography”—a process of image creation and alteration that gives rise to multilayered stories capable of extending beyond the bounds of the ordinary. In this context, photography foregrounds the intuitive, imaginative, and embodied ways of knowing central to the practice of magic, making visible that which may otherwise remain invisible. These digital images thus become relationally constituted material surfaces where networks of bodies, objects and energies are visualized, revealing the porous boundaries between categories such as human and nonhuman, internal and external, material and immaterial, and mundane and magical.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it