Materializing Magic: How the Witches of Instagram Make the Invisible Visible Through Digital Photography and Editing Techniques
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.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".