Bibliographic record
Abstract
Lionello Perera has been a largely forgotten and obscure figure in Italian-American history.Diego Mantoan, an art historian who teaches at the University of Palermo, aims to make Perera more well known in this largely sympathetic biography.Given access to family archives via one of Perera's grandchildren, Mantoan paints a picture of the Italian-American community in New York through this Venetian banker who emigrate to the United States in the late nineteenth century.Significantly, Perera did not emigrate through economic need -unlike most Italian immigrants -but due to a job opportunity offered by his uncle who had started the first Italian-American bank in the 1860s.Rather than give a chronological biography of Perera, Mantoan chooses to divide his book into the main activities of his protagonist.They are banker, philanthropist, patron, and representative of the Italian-American community.Born and raised in Venice, Perera hailed from an Italian-Jewish family of Sephardic ancestry.Following in the family tradition of banking, Perera was an early graduate of the University of Venice's business program at Ca' Foscari.His uncle, Salvatore Cantoni, had immigrated to America in the 1860s where he established a successful bank that catered to the growing Italian-American community.In the 1890s, Cantoni convinced his recently graduated nephew to come and work for him.Combing through the family papers and contemporary news reports, Mantoan suggests that there was a sense of urgency in Cantoni's request as he was enveloped in a potential scandal involving an extramarital affair that jeopardized the future of his bank.Hoping to keep the bank out of the hands of his sons-in-law, who were not Italian, Cantoni tapped Lionello as his successor.Mantoan's account of Lionello's business, which he inherited from Cantoni after his uncle's sudden death, gives us a glimpse into the wild world of early-twentieth-century banking.Despite the lack of business records, Mantoan uses contemporary press accounts to demonstrate Perera's success in building on his uncle's bank to the point of opening a second branch in Harlem.In the buildup to the Wall Street Crash of 1929, Perera's bank was not immune to the alternating fortunes of the largely unregulated world of American banking.In 1926, there was a run on the bank, which forced Perera to directly intervene by hauling thousands of dollars of cash bills across town
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.119 | 0.015 |
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".