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Record W7038466004

Il viaggio di Ernest Duvergier de Hauranne nell'America di Lincoln. Riflessioni sui vizi e le virtu della democrazia.

2020· book-chapter· it· W7038466004 on OpenAlexaboutno aff

Bibliographic record

VenueNova Science Publishers (Nova Science Publishers, Inc.) · 2020
Typebook-chapter
Languageit
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsHastaTime linePeriod (music)
DOInot available

Abstract

fetched live from OpenAlex

Nel giugno del 1864 il giovane liberale francese Ernest Duvergier de Hauranne (1843-1877), cresciuto in una famiglia rispettosa delle tradizioni parlamentari della monarchia orleanista, inizia il suo viaggio verso gli Stati Uniti. In otto mesi di viaggio – con la guerra civile ancora in corso e le elezioni presidenziali che avrebbero riconfermato Lincoln alla presidenza – percorrerà gli Stati Uniti e il Canada proseguendo verso Cuba e le Antille. Quell’itinerario americano, che gli procurerà una lesione polmonare che sarà la causa della prematura morte avvenuta a soli trentaquattro anni, resterà documentato nelle lettere e note, pubblicate in dodici articoli nella «Revue des Deux Mondes» e raccolte nel 1866 con il titolo Huit mois en Amérique (1864-1865). A trent’anni di distanza dalla “prima” Démocratie en Amérique di Tocqueville, Duvergier de Hauranne tornava a esaminare il modello istituzionale americano senza, tuttavia, operare un’esplicita comparazione con la Francia. Nonostante le aperture liberali del Secondo Impero, il persistere della censura suggeriva al giovane analista politico la prudenza di non condannare apertamente quel regime politico, come invece emergerà negli scritti pubblicati nell’ultimo periodo del «governo personale» di Napoleone III, nei quali difenderà apertamente la democrazia rappresentativa e la libertà come segni di progresso e condizione naturale di tutte le nazioni civili.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0350.005

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.

Opus teacher head0.037
GPT teacher head0.265
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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