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Record W4411072389 · doi:10.54254/3049-7248/2025.23519

Comparative analysis of public and private secondary school education systems in the United States

2025· article· en· W4411072389 on OpenAlexaff

Bibliographic record

VenueJournal of Education and Educational Policy Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolitical sciencePublic administrationPrivate schoolSchool systemState (computer science)Mathematics educationSociologyPedagogyPsychologyComputer science

Abstract

fetched live from OpenAlex

The American secondary education system is defined by a distinctive dual-track structure, where public and private high schools operate in parallel. These two systems demonstrate significant disparities in their governance and funding structures. These structural differences not only influence the experience and formation processes of the student but also have extensive implications for the achievement of educational equity and resource allocation. This research compares public and private high schools in the United States with three dimensions: analyzing teacher characteristics, student demographics, and curricula approaches, using National Centre for Education Statistics (NCES) data and academic research to explore key differences and their causes. These findings illustrate three key differences between educational systems. First, teacher certification and professional autonomy differ significantly, with public schools emphasizing formal standardized requirements and private schools offering more flexibility. Second, student demographics and academic outcomes differ, as public schools serve more diverse socioeconomic backgrounds, whereas private schools often have higher academic performance because of higher socioeconomic factors. Third, curriculum and assessment approaches diverge, as public schools focus on strict accountability and standardization, while private schools emphasize more operational autonomy and offer specialized curricula tailored to specific educational philosophies or student needs.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.211
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.458
Teacher spread0.377 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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