Ghosts in the machine: algorithmic fascism and the psychopolitics of crisis
Bibliographic record
Abstract
This article examines the emergence of ‘algorithmic fascism’ as a new form of social control operating through digital infrastructures in the context of global polycrisis. Drawing on Félix Guattari’s schizoanalytic framework, we argue that contemporary fascism thrives in a ‘grey zone’ where subjectivity is fractured into data points and monetised through algorithmic governance. Unlike historical fascism’s centralised propaganda, today’s digital authoritarianism operates through decentralised networks that blur distinctions between user agency and automated control. These systems engineer crisis by leveraging psychosocial fragmentation to normalise authoritarian desires. Through a psychosocial lens, we investigate how digital platforms reshape collective anxiety, erode democratic resistance and facilitate micro-fascist logics. We propose a ‘post-media praxis’ of tactical resistance that hijacks algorithmic systems to counter far-right movements and reclaim ambiguity as a site of struggle against algorithmic subjection.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".