Temporalization and the Digital Vigilante: Past Presencing, Un/Doing Futures and “Jewish Revenge” as Affective Justice in Talia Lavin’s Culture Warlords
Bibliographic record
Abstract
This paper examines the figure of the hate-fighting digital vigilante as embodied through Aryan Queen, an online persona developed and depicted by self-proclaimed antifa member Talia Lavin in her book Culture Warlords. One chapter in the 2020 memoir relays Lavin’s pursuits to elicit and make known identifying information of Der Stürmer, an anonymous white supremacist online hater. I first locate Lavin’s undertaking in the porous policy landscape regulating online hate transnationally to make a case for its value as an entry into the navigation of hate on Telegram, a platform that has become a popular enclave for hate, and one that remains otherwise impenetrable to state efforts at formal governance. I then introduce the digital vigilante as a cultural figure that has become increasingly distinguished from, but developed in relation to, the classical or analogue vigilante in academic literature, albeit with only limited attention paid to the seemingly boundless temporality that constitutes the virtual sphere. Attending to processes of temporalization, I argue, can well serve an analysis of the moral universe within which the digital vigilante operates, thereby enabling a critical engagement with the motivations, methods, and intentions of her justice pursuits online. With the support of anthropological theories of temporalization – namely, past presencing, un/doing futures, and affective justice – I show that justice pursuits by way of digital vigilantism for Lavin are entangled with an affective longing for revenge, and manifest a complex intermingling of open wounds from injustices that emerge from and produce entanglements of the past, present, and future.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".