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Record W7071582810

Understanding Societal Investments in Children

2023· other· en· W7071582810 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersYork University
KeywordsCash transfersCashControl (management)CertificationConfidentialityTax creditChild support
DOInot available

Abstract

fetched live from OpenAlex

The support that society provides to children from low-income families plays a critical role in helping them grow and thrive. This support encompasses a wide range of policies, including increased funding for education and direct assistance to families in the form of cash or in-kind benefits. While it is well established that these forms of support significantly improve child outcomes, questions remain about the most effective strategies for targeting, distributing, and designing programs to maximize the impact of these resources. This dissertation focuses on deepening our understanding of three specific types of social supports for low-income children: education policies that target resources to schools serving low-income students, social policies that provide unconditional cash transfers to low-income families, and policies that focus on improving the environmental conditions of schools. \nFirst, many education policies depend on valid measures of school economic disadvantage. Recent research raises questions about the validity of commonly used free-or-reduced-price-lunch measures, particularly in light of the increasing availability of universal free school meals. The first study links confidential federal tax return data and program participation data housed at the U.S. Census Bureau to examine the validity of several measures of school economic disadvantage. Results suggest that direct certification measures provide the best widely available measure, both over time and across the distribution of school poverty. \nSecond, parental spending on children is important for child development. Using data from a randomized control trial of an unconditional cash transfer to low-income mothers of young children, the second study examines the extent to which the cash transfer is spent on goods directly related to children, relative to other sources of household income. I find that the unconditional cash transfer is more likely to be spent on children than any other household income source, including mothers’ earned income alone. The results suggest that money in the household is differentiated for spending on children. \nFinally, no level of lead is safe in a child’s blood. Moreover, low-income children have higher blood lead levels than non-economically disadvantaged children. Most research and policy has focused on lead abatement in home environments. However, researchers estimate that 73 percent of schools have lead in the drinking water. The third study uses school water lead data linked to education administrative records to estimate a causal effect of school water lead exposure on educational outcomes. The results suggests that water lead exposure may negatively affect students, although the effect is sensitive to model specification. In addition, students’ exposure to lead in schools is curiously correlated with students’ prior achievement, making a causal effect of lead in schools unclear. \nMy dissertation concludes with a discussion of themes and lessons from the three studies for improving social policies to support low-income families.\n

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.038
GPT teacher head0.260
Teacher spread0.222 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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