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Record W4386327196 · doi:10.3102/0013189x231194307

Wealth-Based Inequalities in Higher Education Attendance: A Global Snapshot

2023· article· en· W4386327196 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducational Researcher · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsUniversity of Toronto
FundersEconomic and Social Research Council
KeywordsInequalityAttendanceSnapshot (computer storage)Demographic economicsEducational inequalityHigher educationNational wealthEconomic inequalityEconomicsEconomic growthMathematicsComputer science

Abstract

fetched live from OpenAlex

This study provides a comprehensive global snapshot of wealth-based inequalities in higher education attendance. We draw on data from 117 countries to describe cross-national patterns in higher education attendance rates, disaggregated by wealth quintile and country income group. We then calculate four different indicators to quantify the size of wealth-based inequality in higher education attendance and completion for each country. Our findings point to large wealth-based inequalities in higher education attendance cross-nationally, which are: substantially larger than inequalities in secondary completion, larger in low- and middle-income countries than high-income countries, and negatively associated with national wealth. The results serve as a foundation for future studies on how country-level factors and policies exacerbate or reduce wealth-based inequalities.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.350
GPT teacher head0.514
Teacher spread0.164 · 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