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Record W3024408208

Who Benefits from Selective Education? Evidence from Elite Boarding School Admissions.

2019· article· en· W3024408208 on OpenAlexaboutno aff
Ying Shi

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsRegression discontinuity designDisadvantagedEliteQuarter (Canadian coin)PsychologyMathematics educationDemographic economicsPolitical scienceGeographyEconomicsEconomic growthMathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

Existing research finds minimal gains from attending elite US secondary schools. This paper estimates the causal effect of attending a selective public boarding school, an institutional model increasingly used by states to serve academically gifted students. Regression discontinuity estimates using multiple admissions thresholds show math score gains and college application and enrollment patterns that shift away from less competitive colleges. Effects are concentrated among minorities, students with lower prior individual achievement, from rural neighborhoods, or lower-achieving sending schools. The opportunity to attend selective boarding schools reduces the tendency of disadvantaged or under-represented students to attend a less selective college by at least one-quarter.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.368
Teacher spread0.329 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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