Bibliographic record
Abstract
I n the last chapter, I argued that Manzoni’s novel advocated conversion to Catholicism as a means of establishing a modern Italian nationstate that retained its Catholic identity. It may seem surprising to suggest that a liberal Catholic like Manzoni would vigorously pursue conversion. And yet this was not unusual among neo-Guelph and liberal Catholic thinkers. Vincenzo Gioberti advocated conversion with his vision of an Italian nation-state based on Christian principles. Carlo Maria Curci, a Jesuit opponent of Gioberti, criticized the Giobertian conflation of Catholicism and nationhood, stating that Giobertian ideology was one in which “in the name of the Gospel, Parliament is proclaimed, in the name of the Pope, the Italian confederation, and in the name of Christian morals, the expulsion of the Germans from Lombard-Veneto.” 1 While Gioberti condemned the persecution of religious minorities, his argument for open-mindedness was itself aimed at conversion. That is, rather than be persecuted, he argued that Jews be treated with Christian charity, since such kindness would lead them to convert of their own accord. Similarly, the liberal Catholic Giovanni Vicini criticized the Church for its excessive force in dealing with the Jewish community. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 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 | 0.010 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".