A Gestalt perspective on Manichaean worldviews and individuals’ engagement in violence: the case of the Italian far left
Bibliographic record
Abstract
Besides socio-political and economic factors, extant research contends that Manichaean worldviews, characterized by mutually exclusive dichotomies such as ‘good-bad’, are the main driver influencing individuals’ decision to use violence against others. Furthermore, extant scholarship identifies ideologies, populism, and conspiracy theories as the three originators of Manichaean worldviews. However, the findings from my research, carried out between 2018 and 2023, challenge these arguments. Using narrative analysis, this article examines personal stories of a group of Italian former far-left militants, who participated in the violent campaign of the so-called ‘Years of Lead’. Far-left and far-right ideologies strongly influenced Italian socio-political movements of the time. Thus, this paper explores whether Manichaean perspectives informing far-left militants’ decision to resort to violence originated from far-left ideologies or whether they existed independently of these ideologies. I develop this analysis through the lens of Gestalt psychology, which considers human behavior as resulting from how our minds understand the relation between components of our surrounding environment. While confirming relations between Manichaean worldviews and violence, this paper finds that Manichaean perspectives result from human cognitive processes and are then rigidified by ideological narratives. This work provides important insight to better understand radicalization and engagement in violence, and to develop appropriate responses to prevent it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".