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Record W4401148389 · doi:10.1177/00207152241266791

Participation in shadow education and academic performance: A comparison of upper secondary school students in Ireland and Germany

2024· article· en· W4401148389 on OpenAlexvenueno aff
Robin Benz, Merike Darmody, Emer Smyth

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
FundersH2020 Societal Challenges
KeywordsShadow (psychology)Context (archaeology)Longitudinal studyAcademic achievementSecondary educationPolitical scienceMathematics educationPsychologyDemographic economicsEconomicsGeographyMedicine

Abstract

fetched live from OpenAlex

This article uses two longitudinal cohort studies (Growing Up in Ireland and the National Educational Panel Study) to examine how shadow education relates to academic performance in Ireland and Germany. Patterns of take-up of, and outcomes from, shadow education are found to reflect the particular country context—aimed at maintaining performance to avoid grade retention in Germany and preparing for a high-stakes upper secondary exam in Ireland. Participation enhances academic performance but only for students with lower levels of prior achievement. However, the relationship is not much stronger than with engagement in structured out-of-school activities. Thus, shadow education appears to be one of a number of strategies used by more privileged families to secure educational advantage.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.493
Teacher spread0.435 · 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
Published2024
Admission routes1
Has abstractyes

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