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Record W2938439621 · doi:10.1111/ecca.12307

What Drives Enrolment Gaps in Further Education? The Role of Beliefs in Sequential Schooling Decisions

2019· article· en· W2938439621 on OpenAlexaff
Chris Belfield, Teodora Boneva, Christopher Rauh, Jonathan Shaw

Bibliographic record

VenueEconomica · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversité de Montréal
FundersEconomic and Social Research Council
KeywordsSocioeconomic statusConsumption (sociology)PerceptionValue (mathematics)Sample (material)PsychologySocial psychologyVariation (astronomy)SociologyDemographySocial science

Abstract

fetched live from OpenAlex

We study students’ motives to obtain sixth form and university education in a sample of 885 secondary school students in the UK. At each educational stage, perceptions about the consumption value of education explain a substantial share of the variation in students’ intentions to obtain further education, while beliefs about the monetary benefits and costs are not found to play an important role. Beliefs about the consumption value of university predict not only students’ intentions to go to university but also their intentions to go to sixth form, highlighting the importance of dynamic considerations in the choice. We further document that students’ beliefs about the consumption value of further schooling strongly predict students’ perceptions about how likely it is that they will obtain the necessary grades to proceed to the next educational stage. Differences in the perceived consumption value across gender and socioeconomic groups can account for a sizeable proportion of the gender and socioeconomic gaps in students’ intentions to pursue further education as well as in their perceptions about their own performance.

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)0.012
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.015
GPT teacher head0.354
Teacher spread0.339 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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