<i>Dalla Vostra Parte</i>: populist irony, illiberal ventriloquism, and the rise of the Non Buono refugee in contemporary Italy
Bibliographic record
Abstract
Alongside the highly mediatized life of recent Italian politics, populism as a mode of social expression has permeated Italian public discourse since the mid-1990s. This trend has become even more apparent in the aftermath of the so-called Mediterranean refugee crisis in the 2010s. Since then, the Italian popular classes’ increasing engagement with online social media has contributed to the electoral success of anti-immigration leaders and Facebook celebrities like Lega Nord party secretary Matteo Salvini. Simultaneously, controversial black Italian YouTubers like the artist Bello FiGo have also found commercial success, due to their own populist and ironic engagement with Italian immigration policies. This article explores how ideologically divergent media operators such as Salvini and Bello FiGo co-participate in ironic, future-oriented media performances of anti-refugee discourses that make possible different modes of displaced alterity. As we will demonstrate ethnographically, these performances allow for forms of cultural intimacy between these media operators and their publics by means of populist irony, while engendering opposite (though structurally similar) dynamics of illiberal ventriloquism. In doing so, these controversial, future-oriented performances tend to subvert institutionalized liberal narratives of crisis and systemic displacement and put into question Italian immigration policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".