MétaCan
Menu
Back to cohort
Record W4386934840 · doi:10.1177/00207152231198434

School segregation, student achievement, and educational attainment in Hungary

2023· article· en· W4386934840 on OpenAlexvenueno aff
Zoltán Hermann, Dorottya Kisfalusi

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
FundersNational Research, Development and Innovation OfficeHorizon 2020 Framework ProgrammeNemzeti Kutatási Fejlesztési és Innovációs HivatalEuropean Commission
KeywordsEducational attainmentPovertyMatching (statistics)PsychologyReading (process)Test (biology)Academic achievementScale (ratio)Achievement testStudent achievementPropensity score matchingMathematics educationDemographic economicsDevelopmental psychologyStandardized testPolitical scienceEconomic growthEconomicsGeographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Using large-scale administrative data from Hungary, we examine the effects of attending a high-poverty school in Grade 8 on academic achievement and later educational attainment, using a matching approach. We find that attending a high-poverty school is negatively associated with reading scores and secondary education attainment, while there is no significant association with math scores. Estimates are negative in the case of higher education enrollment, but their statistical significance depends on model specification. We find suggestive evidence that attending a high-poverty school has a large direct negative effect on educational attainment, over and above the indirect effect through lower test scores. This suggests that the negative effect of high-poverty schools on students’ noncognitive skills and later educational choices can be as important as the effect on achievement.

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)0.000
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 score0.038
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.448
Teacher spread0.387 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Comparative SociologySame topicSchool Choice and PerformanceFrench-language works237,207