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Crime and the Life-Course, Prevention, Experiments, and Truth Seeking: Joan McCord's Pioneering Contributions to Criminology

2018· article· en· W2892614438 on OpenAlexaff
Richard E. Tremblay, Brandon C. Welsh, Geoffrey Sayre‐McCord

Bibliographic record

VenueAnnual Review of Criminology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCriminologyLife course approachJuvenile delinquencyIntervention (counseling)Punishment (psychology)SociologyLife spanPsychologyDevelopmental psychologyGerontologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

A life-span developmental approach describes Joan McCord's career and highlights her pioneering contributions to criminology and, more broadly, to understanding human development. The main focus of this article is on her exceptional scientific contributions through the assessment of the Cambridge-Somerville Youth Study experimental preventive intervention. We highlight her efforts to understand how a delinquency prevention intervention caused iatrogenic effects and the lessons she drew for evaluation research. Important contributions to key issues in developmental criminology are summarized, such as the different roles of mothers, fathers, and neighborhoods in the development of delinquency as well as the importance of differentiating discipline from punishment. We describe how Dr. McCord relied on philosophy, how she tackled oppositions between theory-driven and data-driven research in criminology, and how she helped young investigators learn how to learn, and we end by highlighting her contributions to the organization and development of criminology in the United States and around the world.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.430
Teacher spread0.347 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2018
Admission routes1
Has abstractyes

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