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Record W4390893728 · doi:10.3386/w32045

The Impact of Comprehensive Student Support on Crime: Evidence from the Pathways to Education Program

2024· report· en· W4390893728 on OpenAlexafffundabout
Adam Lavecchia, Philip Oreopoulos, Noah Spencer

Bibliographic record

VenueNational Bureau of Economic Research · 2024
Typereport
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersEmployment and Social Development Canada
KeywordsPsychologyMathematics educationMedical educationMedicine

Abstract

fetched live from OpenAlex

This study finds substantial reductions to criminal activity from the introduction of a comprehensive high school support program for disadvantaged youth living in the largest public housing project in Toronto.The program, called Pathways to Education, bundles supports such as regular coaching, tutoring, group activities, free public transportation tickets and bursaries for postsecondary education.In this paper, we use a difference-in-differences approach that compares students living in public housing communities where the program was offered to those living in communities where the program was not offered over time.We find that eligibility for Pathways reduces the likelihood of being charged with a crime by 32 percent at its Regent Park location.This effect is driven by a reduction in charges for breaking and entering, theft, mischief, other traffic offenses and Youth Criminal Justice Act offenses.

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)0.008
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.524
GPT teacher head0.625
Teacher spread0.101 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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