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Record W1602460230 · doi:10.3386/w20430

Pathways to Education: An Integrated Approach to Helping At-Risk High School Students

2014· report· en· W1602460230 on OpenAlexaffabout
Philip Oreopoulos, Robert S. Brown, Adam Lavecchia

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typereport
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversité LavalUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsMathematics educationPsychologyMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Pathways to Education is a comprehensive youth support program developed to improve academic outcomes among those entering high school from very poor social-economic backgrounds. The program includes proactive mentoring to each student, daily tutoring, group activities, career counseling, and college transition assistance, combined with immediate and long-term incentives to reinforce a minimum degree of mandatory participation. The program began in 2001 for entering Grade 9 students living in Regent Park, the largest public housing project in Toronto, and expanded in 2007 to include two additional Toronto projects. In all three locations, participation rates quickly rose, to more than 85 percent, even though parents and students were required to commit in writing to conditions and high expectations of the program. Comparing students from other housing projects before and after the introduction of the program, high school graduation and post secondary enrollment rates rose dramatically for Pathways eligible students, in some cases by more than 50 percent.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0160.003

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 head0.398
GPT teacher head0.574
Teacher spread0.175 · 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
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

Citations56
Published2014
Admission routes2
Has abstractyes

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