Right-wing extremism and the limits of community in Zerocalcare’s <i>This World Can’t Tear Me Down</i>
Bibliographic record
Abstract
In this article, I critically consider the representation of right-wing extremism in This World Can’t Tear Me Down , a semi-autobiographical animated television series by the Italian graphic artist Zerocalcare. In sustained dialogue both with Zerocalcare and leading Italian political philosopher Roberto Esposito and others, I explore the origins and nature of community with a special focus on what follows from the practical impossibility of containing and expelling extremism from contemporary social and political life. My argument focuses on the importance of ‘immunity’ to establishing and maintaining a community. For Esposito, communities come into being as a consequence of immunitory practices which, among other things, mark the boundaries between what is safe (and thus socially acceptable) and what is unsafe, as well as between the familiar and the strange. While immunization does positive work by marking the limits of a community in order to protect its members, I will explain how it does so not by isolating and removing threats to a collectivity’s vitality and integrity, but rather by introducing into collective life some measure of precisely that which threatens to undermine and destroy it. It is only via such a ‘homeopathic protection practice’ that a community becomes equipped to survive, and its members able to thrive and live their lives fully.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".