Myths and Counter-Myths of America. New World Allegories in 20th–Century Italian Literature and Film
Bibliographic record
Abstract
1860s.In this introduction, rather than immediately after the selection itself, are also to be found the sources for the material chosen by the editor, although some further details are given in the division entitled "About the Authors", which appears after the last "Testimony".The volume is concluded with a "Selected Bibliography" (of two pages), a "Selected Film Chronology" (one page), and a bibliography on "History and Criticism" (three pages), all of which are most useful and would make the volume a valuable addition to courses on Italian culture and history.Unfortunately, the quality of the English into which the original Italian has been translated makes such a recommendation difficult, if not impossible: the editor's English prose is vitiated by errors of vocabulary, grammar, and syntax too frequent to be able to be overlooked in an academic review such as this one.Prepositions, admittedly a challenge for many translators, are at times unidiomatic or incorrect (e.g."with their presence," 33; "on their own accord," 107; "at the service of," 145), infinitives are split (e.g."to actively manage," 9), negatives doubled (e.g."did not trust him hardly at all," 88), intransitive and transitive verbs confused (e.g."black pitch that shined like marble," 88), vocabulary misused (e.g."swear it on the saint evangelists," 36; "[he] was reduced to Piazza Rivoluzione," 63; "the murder of an excellent cadaver," 150), and the syntax is at times surprisingly warped.What, for instance, is to be made of a sentence like this: "But my intolerance for the very hypothesis of a punishment inflicted upon me required that I exclude any fear of revenge taken out on my administrator's back, according to the workings of evasion of a form of servitude" (126)?Or this: "Now everything was understandable, that repulsion expressed to her by that maternal voice, which expressed only pleasure, rendered dirty by the surroundings and presence in the midst of which it manifested" (165)?These and other serious shortcomings seem to result from the translator's misplaced preference for literal renderings, which can violate the spirit of the target language; but others, too, may in some measure be responsible for such errors, increasingly common in an age of print-ready copy and overworked in-house editors.I hope the money will be found to produce a revised edition of this interesting volume with the necessary emendations, the sources of the selections more clearly indicated, and perhaps even a snapshot or two by Letizia Battaglia, that passionate modern photographer of the Leopard's timeless lair.D.R.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".