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Record W6921582593 · doi:10.7916/d8-t4zk-ha26

Oral history interview with Ralph Salerno, 1982

2020· article· en· W6921582593 on OpenAlexaboutno aff

Bibliographic record

VenueColumbia Academic Commons (Columbia University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAnarchism and Radical Politics
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyHeroinLaw enforcementUnit (ring theory)Police departmentCrime controlNarcotic

Abstract

fetched live from OpenAlex

In this interview, Ralph Salerno discusses his thirty-six year career in law enforcement, twenty years of which he spent at the New York City Police Department. He discusses his decision to become a police officer, and how his recruitment onto the force landed him in a unit that was investigating the murder of Joseph Scottoriggio, initiating his specialization in organized crime. He discusses being made a detective in 1950. Salerno describes tactics of investigation such as electronic and physical surveillance. He explains the many routes through which heroin and opium entered the United States, including through Canada, South America, and Vietnam. He discusses the impact of the Narcotic Control Act of 1956 on New York drug trafficking. He details an investigation he did in the 1960s into the Gallo-Profaci gang war. Salerno discusses his 1969 book "The Crime Confederation: Cosa Nostra and Allied Operations in Organized Crime." He describes how control of the New York City narcotics trade transitioned from mainly Jewish crime syndicates to mainly Italian crime syndicates between 1930 and 1960. Salerno describes the corruption he perceived was growing in the police force, and how it was a contributing factor in his decision to retire at forty-one years old. Salerno discusses what he believes the future of narcotic policy will be in the future in the United States

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.070
GPT teacher head0.247
Teacher spread0.177 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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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