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Record W1516918368 · doi:10.1111/nyas.12331

Economic perspectives on integrating early child stimulation with nutritional interventions

2014· review· en· W1516918368 on OpenAlexfundno aff
Harold Alderman, Jere R. Behrman, Sally Grantham‐McGregor, Florencia López Bóo, Sergio Urzúa

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2014
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsSocioemotional selectivity theoryPsychological interventionContext (archaeology)Stylized factCognitionChild developmentPsychologyCognitive developmentPublic economicsDevelopmental psychologyEconomicsPsychiatryGeographyMacroeconomics

Abstract

fetched live from OpenAlex

There is a strongly held view that a narrow window exists for effective nutritional interventions and a widely known stylized depiction of age-dependent economic rates of returns to investments in cognitive and socioemotional development. Both indicate critical periods in early life. Moreover, the fact that both the physical and cognitive development of a child in these early years are highly dependent on childcare practices and on the characteristics of the caregivers motivates an interest in finding effective means to enhance stimulation in the context of nutritional programs, or vice versa. Nevertheless, there is relatively little evidence to date on how to align integrated interventions to these age-specific patterns and how to undertake benefit-cost analyses for integrated interventions. Thus, many core questions need further consideration in order to design integrated nutritional and stimulation programs. This paper looks at some of these questions and provides some guidelines as to how the economic returns from joint nutrition and stimulation programs might be estimated.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.127
GPT teacher head0.406
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2014
Admission routes1
Has abstractyes

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Same venueAnnals of the New York Academy of SciencesSame topicChild Nutrition and Water AccessFrench-language works237,207