La lotta di fazione nell'Italia del Duecento: la Firenze di Dante
Bibliographic record
Abstract
Il lavoro di tesi presentato analizza il contesto storico che pone al centro lo scontro tra i guelfi e i ghibellini nell’Italia del Duecento e la formazione delle due fazioni. Il primo capitolo si sofferma sull’origine delle due casate e sugli aspetti che le caratterizzano analizzando gli eventi toscani che fanno da cornice all’elaborato: la battaglia di Montaperti e la morte di Federico II di Svevia. Nel secondo capitolo ci si sofferma sulla dinastia degli Svevi, personaggi fondamentali per la storia del XII-XIII secolo; doverosa è l’attenzione alle figure di Federico Barbarossa e Federico II. Numerosi sono i conflitti che si verificano nel XIII secolo tra papato e impero, il papa chiede il sostegno degli Angioni in Italia per contrastare l’autorità dell’imperatore. L’ultimo capitolo si concentra sulla situazione politica toscana, mettendo in evidenza la figura di Dante. Da qui si crea l’ulteriore divisione interna dei guelfi, che si dividono in bianchi e neri. Si prosegue con la nascita delle signorie, mettendo in evidenza alcune delle più importanti esperienze signorili in Italia e la nascita di nuovi movimenti neoguelfo e neoghibellino. Infine, questo lavoro si pone l’obiettivo di ripercorrere l’evoluzione storica tra il XII e il XIV secolo, intrecciandola con quella letteraria attraverso il ruolo politico di Dante nella Firenze del Duecento. The thesi’s work presented analyses the historical context of the clash between the Guelphs and the Ghibellines in 13th century Italy and the formation of the two factions. The first chapter focuses on the origin of the two factions and on the aspects that characterise them, analyzing the Tuscan events that frame the work: the battle of Montaperti and the death of Frederick II of Swabia. The second chapter we focus on the Swabian’s dynasty, fundamental figures for the history of the 12th and 13th centuries; it is necessary to pay attention to Frederick Barbarossa and Frederick II. There were numerous conflicts that occurred in the 13th century between the papacy and the empire, and the pope asked the support of the Angevins in Italy to counter the emperor’s authority; from this we see the end of the Swabian dynasty and the advent of the Angevins in Italy. The last chapter focuses on the political situation in Tuscany, highlighting the figure of Dante. This leads to some insights into the alliances that are created, but above all into the further internal division of the Guelphs into whites and blacks. We continue with the birth of lordships, highlighting some of the most important lordly experiences in Italy and the birth of new neo-Guelph and neo-Ghibelline movements. In conclusion, this work sets out to trace the history of Italy between the 12th and 14th centuries, intertweaving it with the literary one through Dante’s political role in the Florence of 13th century.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".