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Record W3023722429 · doi:10.3386/w21023

Correlation, Consumption, Confusion, or Constraints: Why do Poor Children Perform so Poorly?

2015· preprint· en· W3023722429 on OpenAlexaff
Elizabeth M. Caucutt, Lance Lochner, Young‐Min Park

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsConfusionConsumption (sociology)CorrelationPsychologyComputer scienceMathematicsSociologySocial sciencePsychoanalysis

Abstract

fetched live from OpenAlex

The economic and social mobility of a generation may be largely determined by the time it enters school given early developing and persistent gaps in child achievement by family income and the importance of adolescent skill levels for educational attainment and lifetime earnings.After providing new evidence of important differences in early child investments by family income, we study four leading mechanisms thought to explain these gaps: an intergenerational correlation in ability, a consumption value of investment, information frictions, and credit constraints.In order to better determine which of these mechanisms influence family investments in children, we evaluate the extent to which these mechanisms also explain other important stylized facts related to the marginal returns on investments and the effects of parental income on child investments and skills.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.430
GPT teacher head0.543
Teacher spread0.112 · 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
Published2015
Admission routes1
Has abstractyes

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