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Record W4387673280 · doi:10.1111/ecin.13182

Race and the Income‐Achievement Gap

2023· article· en· W4387673280 on OpenAlex
Ryan Bacic, Angela Zheng

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEconomic Inquiry · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaMcMaster University
KeywordsSocioeconomic statusRace (biology)Demographic economicsIndigenousFamily incomeEconomic inequalityInequalityEconomicsPsychologyDevelopmental psychologyDemographyEconomic growthSociologyGender studies

Abstract

fetched live from OpenAlex

Abstract A large literature documents a positive correlation between parental income and child test scores. In this paper, we study whether this relationship, the dependence of the cognitive skills of children on the socioeconomic resources of their parents, varies across race. Using education data linked to tax records, we find that the income‐achievement gap is small for East Asian children while significantly larger for Indigenous children. School‐level factors explains a large portion of the variation in the gap across race. Our results suggest that the large income‐achievement gap for Indigenous students stems partially from inequality in special needs diagnoses.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.054
GPT teacher head0.335
Teacher spread0.280 · 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