Bibliographic record
Abstract
This paper analyzes the work of actor/writer Marco Paolini, and his acclaimed monologue Il racconto del Vajont in particular. In the wake of Dario Fo and Franca Rame’s teatro civile, Paolini’s monologues contributed to the birth of the so-called teatro di narrazione in the 1990s, which can also be defined as “theatre of trauma”, that is, a theatre that recovers the memory of tragic events from the past. In recent times, trauma has become a central theme of Western narrative, politics, and other forms of representation in the public sphere. Following the thought of philosophers such as Jean-François Lyotard and Slavoj Zižek, the article reads Paolini’s Il racconto del Vajont as a significant example of what writer Kyo Maclear calls “witness art.” Characterized by a crisis of the traditional models of representation in mainstream culture, witness art is conceived by Maclear in opposition to the traditional divisions between art, knowledge, and the political instances of public discourse. Among Paolini’s many performances of Vaiont, the one performed at the same time and place where the tragedy took place 34 years before, and broadcasted live on RAI 2 in 1997, stands out for its uniqueness. That night, Paolini evoked the reenactment of a traumatic experience by its witnesses, and, on a wider scale, by the audience watching television at home. An almost forgotten tragedy became a media event, and for the first time trauma witnessing as such—and not as a means for a specific political claim—became part of the public discourse in the elaboration of post-1989 Italy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".