Witches in Swamps, Sirens at Sea, and Leviathans of the Deep: Feminist Figures that Haunt our Social Media Worlds
Bibliographic record
Abstract
This article considers the resurgence of the monstrous feminine in digital culture, focusing on the memetic manifestation of witches, sirens, and leviathans. We trace the histories of these figures in the digital present, dwelling with their legacies to map and document the feminist resistance that has emerged in response to the neoliberal, misogynist culture of digital space that seeks to stamp out transgression. These media ecologies of the internet thrive on the networked misogyny, white supremacy, and polarization of their systems, brought in by the tech bros who coded them. From within these conditions, we illustrate how digital witches, sirens, and leviathans fight back, offering resistance that transcends digital bounds. In the face of the misogyny of digital culture at large, we argue that each of these figures linger, haunting as they embody, ground, and serve anti-capitalist feminist resistance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".