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Alternative Models of Entrance Exams and Access to Tertiary Education: A Simulation Study

2010· article· en· W1522619813 on OpenAlexaboutno aff
Tomáš Konečný, Josef Basl, Jan Mysliveček

Bibliographic record

VenueCzech Sociological Review · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsAptitudeContext (archaeology)CzechSocioeconomic statusQuarter (Canadian coin)OddsHigher educationMathematics educationMargin (machine learning)PsychologyField (mathematics)DemographyMathematicsEconomicsStatisticsSociologyComputer scienceDevelopmental psychologyGeographyPopulationEconomic growth

Abstract

fetched live from OpenAlex

The study evaluates the potential impact of alternative models of university entrance exams - a model based on field-specific knowledge and a model relying on general aptitude tests - in the context of the Czech education system since 1998, a system that can be described as highly stratified and suffering from a notable excess of demand for higher education over supply. Using the dataset Sonda Maturant 1998, the authors show that entrance exams based on general aptitude tests may outperform the field-specific knowledge model in terms of providing access to talented students from a lower socioeconomic background. The simulations show that under the general aptitude regime the relative chances of an applicant with a university-educated father are only one-quarter higher than the relative chances of a student with a less educated father, compared to more than a one-third difference in the case of the regime emphasising field-specific knowledge. For mother's education, the respective odds ratios differ by the even larger margin of 28 percentage points.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.367
Teacher spread0.261 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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