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Record W2950244915 · doi:10.21307/connections-2017-006

The Boston Special Youth Project Affiliation Dataset

2018· article· en· W2950244915 on OpenAlexvenueno aff
Jacob T.N. Young, Scott H. Decker, Gary Sweeten

Bibliographic record

VenueConnections · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)MillerPsychologyCriminal behaviorCriminologySociologyPublic relationsMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract The Boston Special Youth Project (SYP) Affiliation dataset is a large, bipartite network representing interactions among 166 gang members from seven gangs for nearly three years. The project was conducted from June 1954 to May 1957 and represents one of the most elaborate gang intervention programs ever conducted. The SYP was a “detached-worker program,” where an adult (typically a graduate student from one of the surrounding universities) was assigned to an area (local parks, housing projects) to establish and maintain contact with and attempt to change the behaviors of the gangs. These workers collected detailed field notes (“contact cards”) documenting the activities of study gang members. However, the social network data collected on the contact cards were never analyzed by SYP staff. After the death of the project leader, Walter Miller, in 2004, the materials from the project became available to a team of researchers (faculty, graduate, and undergraduate students) in the School of Criminology and Criminal Justice at Arizona State University. These researchers electronically scanned and digitized the contact cards, and began the process of creating a network from the cards. From these cards, a bipartite network was created where 166 individuals (i.e. gang members) were connected to 33,653 events (i.e. contact cards).

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.037
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.022

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 head0.112
GPT teacher head0.411
Teacher spread0.299 · 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 designObservational
Domainnot available
GenreDataset

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

Citations1
Published2018
Admission routes1
Has abstractyes

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