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Record W4249369901 · doi:10.31235/osf.io/hnyd6

Social Inequality in Student Expectations and Higher Education Enrollment - A Comparison between the United States and Germany

2021· preprint· en· W4249369901 on OpenAlexaff
Andrea Förster, Anna K. Chmielewski, Herman G. van de Werfhorst

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEducational attainmentContext (archaeology)Demographic economicsInequalityPanel dataHigher educationRealization (probability)Status attainmentEducational inequalityPolitical sciencePsychologyEconomicsSociologyEconometricsSocioeconomic statusEconomic growthDemographyGeographyStatistics

Abstract

fetched live from OpenAlex

We examine the link between student expectations and educational attainment. While a close link between expectations and final educational attainment is assumed in thestatus attainment literature, a growing body of US literature has claimed that expectations have become increasingly unrealistic and decoupled from actual outcomes. However, this claim has rarely been investigated longitudinally on the individual level or beyond the context of the United States. We investigate the relationship for two educational systems with different institutional configurations: The United States and Germany. For Germany, we use data from the National Educational Panel Study (NEPS, Starting Cohort 4). For the US, we rely on data from the High School Longitudinal Study (HSLS, 2009). We find that the level of expectations is overall much higher in the US; however, we also find that the gap between SES groups is larger in the US. Furthermore, we find that the students in these two countries do not differ much in terms of the probability that they will realize their expectations. Additionally, the SES gradient of realization is fairly similar across the different institutional contexts. Finally, we also find that expectations do mediate a substantial part of the effect of SES on higher

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.983

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 studies0.0010.000
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 head0.148
GPT teacher head0.466
Teacher spread0.318 · 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
Published2021
Admission routes1
Has abstractyes

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