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Record W2592942942 · doi:10.22230/ijepl.2016v11n5a674

Paying for School Choice: Availability Differences among Local Education Markets

2016· article· en· W2592942942 on OpenAlexvenueno aff
Jin Lee

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSchool choiceMetropolitan areaGovernment (linguistics)Distribution (mathematics)Public economicsRegression discontinuity designEconomic growthBusinessEconomicsGeographyMarket economy

Abstract

fetched live from OpenAlex

On the grounds of the school zone discontinuity by parents’ educational level, housing price, and household income, empowering parents to choose children’s schools with their own hands has the potential to improve overall access to education by weakening geographical advantages, or disadvantages, and opening up invisible boundaries between communities. Though recent school choice proposals seem aligned with issues of access to education, little research has paid attention to potential access to and actual utilization of the federal government-initiated choice program in competitive markets. This paper explores whether or not the markets for the public school choice provision under the No Child Left Behind Act of 2001 are ready to serve students at chronically underperforming schools, by representing the geographic distribution of choice availability in a segregated metropolitan area. This study finds that the public school choice provision under the NCLB builds unequal choice settings between school districts.

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)0.003
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.106
GPT teacher head0.395
Teacher spread0.289 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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