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Record W4402909829 · doi:10.1016/j.dr.2024.101163

Children’s saving: A review and proposed ecological framework

2024· review· en· W4402909829 on OpenAlexafffund
Ege Kamber, Cristina M. Atance, Deepthi Kamawar, Caitlin E. V. Mahy

Bibliographic record

VenueDevelopmental Review · 2024
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsCarleton UniversityUniversity of OttawaBrock University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyEcology

Abstract

fetched live from OpenAlex

• Children begin to save money and items at 3 years of age. • Children’s saving is related to cognitive abilities, personality, and social factors. • Short-term interventions or education programs can facilitate children’s saving. • We propose a novel ecological framework to better understand children’s saving. • Personal characteristics, the environment, and their interactions play a role in the development of children’s saving. Saving, defined as reserving current resources for future use , is a valuable future-oriented skill that allows individuals to meet their future goals (e.g., retire, go on vacation) without experiencing resource scarcity, disappointment, or distress. To date, saving has been examined extensively in adults, but to a lesser extent in childhood. Over the past decade, a small but growing body of research has focused on the early development of saving and has shown that children as young as age 3 can save for the future. In this paper, we review the literature on individual differences in children’s saving in relation to cognitive abilities, personality traits, and social environments (e.g., home environment and societal factors). Then, we propose an ecological framework of saving as a theoretical ground to examine children’s ability to save and to conceptualize how various factors, and their interactions, shape the development of saving and lead to (mal)adaptive saving habits. We conclude by suggesting important future directions for research that would further test this ecological framework.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.307
Teacher spread0.272 · 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
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

Citations2
Published2024
Admission routes2
Has abstractyes

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